Method and apparatus for camera calibration

ABSTRACT

An imaging system is provided comprising: a memory storing instructions which, when executed by processing circuitry, cause the processing circuitry to perform operations comprising: determining multiple image regions of interest (ROIs) within a camera image plane that correspond to one or more three-dimensional (3D) world object images; determining multiple radar ROIs that correspond to one or more 3D world objects; determining 3D world distances corresponding to the radar ROIs; determining multiple co-registered ROI pairs by co-registering individual image ROIs with individual radar ROIs corresponding to common 3D world objects; adjusting one or more parameters associated with the camera, based upon the co-registered ROI pairs.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/854,375, filed on Apr. 21, 2020, which is hereby incorporated byreference in its entirety.

BACKGROUND

Geometric Camera Parameters

A camera is an optical instrument to project three-dimensional (3D)world images onto a flat surface referred to as an image plane. Cameraimages may or may not be within the visible light spectrum. Acamera-based vision system characterizes its environment as atwo-dimensional image. A camera can be used to infer the depth or rangeof detected objects. Optical images captured at camera pixels in theimage plane are transformed to real world depth measurements to analyzecertain aspects of a scene, such as the size or position of objects.However, a camera generally must be calibrated periodically withgeometric parameters, also referred to as extrinsic parameters, thatprovide precise camera location and camera pose information required toaccurately infer spatial relations from its images. In stereo camerasystems, this transformation from pixel coordinates to depth informationcan be done with disparity computation between the camera sensors afterbasic calibration, and in monocular systems this typically can be donewith prior scene parameters/rules or with machine learning methods knownin the art. In a mono camera system, for example, camera height andangle relative to a ground plane must be known (or calibrated for)precisely in order to infer high quality depth maps. In a multi-camerasystem, for example, the relative distance and pose of the cameras withrespect to one another must be known (or calibrated for) precisely inorder to infer high quality depth maps.

Calibration of a multi-camera system typically is primarily to estimatea mathematical matrix that maps between a 3D world and each individualcamera view. The matrix mapping includes geometric parameters thatprovide translation distances and relative angles between cameras in amulti-camera system. Calibration of geometric parameters has six degreesof freedom in terms of translation and rotation between two cameras. Amapping or reprojection between camera views also can be inferred fromthese matrices. A perfect calibration results in zero reprojection erroramong the camera views. Often, geometric calibration is performed usinga checkerboard pattern placed at some known distance. In general,however, any 3D world features with some prior knowledge of the patterncan be used, e.g., size of the squares in a checkerboard pattern.

FIGS. 1A-1B are illustrative drawings showing an example stereo camerasystem including a left camera 102 and a right camera 104 with camerashorizontally aligned parallel in an x-axis (FIG. 1A) and showing anexample stereo camera system with a relative rotation of the rightcamera 104 such that the left and right cameras 102, 104 are misalignedin the x-axis (FIG. 1B). In both the aligned and misaligned examples, animage of an object 106 in a 3D world is projected on a left camera imageplane 108 and a right camera image plane 110. For the horizontallyaligned cameras example in FIG. 1A, a z-distance of the object from thecameras can be calculated as,

Z=BF/(xL−xR)  (1)

where Z is the distance from the object computed based upon geometricparameters, B is distance between the cameras, F is focal length, xL isx-axis offset of the object image within the left camera, and xR isoffset of the object image within the right camera. For the misalignedcamera image planes example in FIG. 1B, an unknown, angular offset inthe misaligned (right) cameras example will correspond to a shift (dx)of the object in the (right) image plane FIG. 1B. In this misalignedstereo camera example, without camera calibration, if we assume zerorotation between the cameras, the z-distance of the object from thecameras will be calculated as,

Z_hat=BF/(xL−xR−dx)  (2)

Taking into consideration the offset on the right side as xR+dx, Z_hatis the computed distance of the object based on the uncalibrated, zerorotation geometric parameters. In this example Z_hat<Z. Thus, a small,unknown (uncalibrated) rotation of the cameras with respect to eachother influences the accuracy of the z-direction measurement of thecameras. In order to resolve this inaccuracy in depth estimation, onemust properly calibrate to determine the precise pose of the cameraswith respect to each other.

Unfortunately, in actual use in the field, various geometric factorssuch as vibrations, mechanical shock, large temperature variations,stress/strain on fixture, for example, can frequently and repeatedlythrow off camera calibration, which can necessitate more frequentcalibration. Moreover, actual distances from 3D world objects in thefield generally are unknown and estimated distance using a camera opticsgenerally is accurate only to a camera scale factor. Therefore, thereexists a need to accurately and quickly adjust geometric parameters usedto calibrate the pose of camera systems in the field where actualdistances are unknown.

Photometric Camera Parameters

A vision system whether comprised of a single camera or multiple camerascan require photometric calibration to achieve acceptable image quality.For a mono-camera the photometric calibration can involve settingphotometric parameters (sometimes referred to as ‘intrinsic’ parameters)such as auto-exposure (AE), auto-white balance (AWB), and auto-focus(AF), (the ‘3A’ parameters), gain, and HDR, for example, to achievevisibility of the scene in varied conditions including mixed lighteningand challenging scenarios (such as WDR, low light, direct sun-light).For a multi-camera system a calibration also can involve photometriccalibration across the camera views to achieve a uniform visualexperience in 3A, gain/tone mapping and HDR. In low light or darkconditions, a multi-camera system is usually coupled with one or moreilluminators (visible and/or IR) and in these conditions a continuousphotometric correction is usually needed between the illuminator powerand the camera's photometric calibration settings to avoid saturation,avoid noisy images and maintain uniformity across views.

SUMMARY

In one aspect, an imaging system is provided that includes a memorystoring instructions which, when executed by processing circuitry, causethe processing circuitry to perform operations. The operations includeusing a camera to receive image information projected at an image planeof the first camera from a 3D world. The operations include determiningmultiple image regions of interest (ROIs) based upon the imageinformation, wherein individual ones of the multiple image ROIscorrespond to one or more three-dimensional (3D) world object imagesprojected at the image plane. The operations include using a radar unitto receive radar information indicating one or more 3D world objects.The operations include determining multiple respective radar ROIs aredetermined that correspond to one or more 3D world objects. Theoperations include determining 3D world distances that correspond to theradar ROIs. The operations include determining multiple co-registeredROI pairs by co-registering individual image ROIs with individual radarROIs corresponding to common 3D world objects. The operations includeadjusting one or more geometric parameters associated with the camerabased upon the co-registered ROI pairs.

In another aspect, an imaging system is provided that includes a memorystoring instructions which, when executed by processing circuitry, causethe processing circuitry to perform operations. The operations includereceiving an image of an object using a camera calibrated using a groupof intrinsic parameters. The operations include determining luminanceinformation using the camera. The operations include, receiving, using aradar unit, radar information indicating an object. The operationsinclude, determining based upon the radar information, a speed toassociate with the object. The operations include adjusting one or moreintrinsic parameters of the group of intrinsic parameters based upon thedetermined luminance information and the tracked speed of the object.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIGS. 1A-1B are illustrative drawings showing an example stereo camerasystem with camera image planes aligned parallel in the x-axis (FIG. 1A)and misaligned in a horizontal (FIG. 1B).

FIG. 2A is an illustrative schematic block diagram of an example imagingsystem architecture with depth sensor-based camera calibration.

FIG. 2B is an illustrative drawing representing location of first andsecond radar ROIs identified within an example radar field of view ofthe imaging system of FIG. 2.

FIG. 2C is an illustrative drawing representing location of first andsecond image ROIs identified within an example camera field of view ofthe imaging system of FIG. 2.

FIG. 3 is an illustrative drawing representing an example known pinholecamera model.

FIG. 4 is an illustrative drawing showing an example single camerasystem architecture with depth-sensor based camera calibration.

FIG. 5 is an illustrative flow diagram representing an examplecalibration process for the example single camera of FIG. 4.

FIG. 6A is an illustrative example simplified diagram of a multi-camerasystem with depth-sensor based camera calibration.

FIG. 6B is an illustrative example drawing showing regions of interestin the first and second camera and in the radar fields of view.

FIG. 7 is an illustrative flow diagram representing an example processto calibrate the first and second cameras of FIG. 6.

FIG. 8 is an illustrative schematic block diagram of an example imagingsystem with radar-based single camera calibration based upon movingobjects.

FIG. 9 is an illustrative schematic block diagram of an example imagingsystem with radar-based multi-camera calibration based upon movingobjects.

FIG. 10 is an illustrative schematic block diagram of an example imagingsystem with radar-based single camera calibration based upon staticobjects.

FIG. 11 is an illustrative schematic block diagram of an imaging systemwith radar-based multi-camera calibration based upon static objects.

FIG. 12A is an illustrative drawing showing an example imaging systemwith depth sensor-based camera calibration that includes a shared fieldof view of a camera and a radar unit in accordance with someembodiments.

FIG. 12B is an illustrative drawing showing a camera-detected objectview visible a camera view and a radar-detected object view visiblewithin a camera view.

FIG. 13 is an illustrative schematic block diagram showing additionaldetails of the imaging system of FIG. 12A.

FIG. 14 is an illustrative block diagram showing certain additionaldetails of an example camera system in accordance with some embodiments.

FIG. 15 is an illustrative drawing showing an example camera imagingsystem that includes radar tracking and camera illumination.

FIG. 16 is an illustrative schematic block diagram showing certaindetails of an example camera imaging system that includes radar trackingand camera illumination.

FIG. 17 is an illustrative block diagram of a computing machine inaccordance with some embodiments.

FIG. 18 illustrates the training and use of a machine-learning program,in accordance with some embodiments.

FIG. 19 illustrates an example neural network, in accordance with someembodiments.

FIG. 20 illustrates the training of an image recognition machinelearning program, in accordance with some embodiments.

FIG. 21 illustrates the feature-extraction process and classifiertraining, in accordance with some embodiments.

DESCRIPTION OF EMBODIMENTS

The following description is presented to enable any person skilled inthe art to create and use a system to use calibration of geometric andphotometric camera parameters based upon information determined usingradar. Various modifications to the embodiments will be readily apparentto those skilled in the art, and the generic principles defined hereinmay be applied to other embodiments and applications without departingfrom the spirit and scope of the inventive subject matter. Moreover, inthe following description, numerous details are set forth for thepurpose of explanation. However, one of ordinary skill in the art willrealize that the inventive subject matter might be practiced without theuse of these specific details. In other instances, well-knowncomponents, processes and data structures are shown in block diagramform in order not to obscure the disclosure with unnecessary detail.Identical reference numerals may be used to represent different views ofthe same item in different drawings. Flow diagrams in drawingsreferenced below are used to represent processes. A computer system maybe configured to perform some of these processes. Blocks within flowdiagrams representing computer implemented processes represent theconfiguration of a computer system according to computer program code toperform the acts described with reference to these blocks. Thus, theinventive subject matter is not intended to be limited to theembodiments shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

Calibration of Geometric Camera Parameters

Imaging System Architecture with Depth Sensor-Based Camera Calibration

FIG. 2A is an illustrative schematic block diagram of an example animaging system architecture 200 with depth sensor-based cameracalibration. The imaging system 200 includes a camera system 202, aradar-based depth sensor 204, and a computing machine described belowwith reference to FIG. 12, including one or more processor devicesconfigured to operatively couple to one or more non-transitory storagedevices that store instructions to configure the processing devices toperform the block functions described as follows.

An example camera system 202 can include one or more cameras to capturetwo-dimensional (2D) image information from a three-dimensional (3D)world scene. A camera pipeline block 206 processes image information andcorresponding tracking information and corresponding classificationinformation to extract image regions of interest (ROIs) that correspondto images of 3D world objects within a scene. An image data buffer block208 stages a sequence of image information frames together withcorresponding tracking and classification information for use ingeometric calibration processing.

An example radar unit 204 can have single transmit/receive (Tx/Rx) unitsor operate as phased array or MIMO with multiple Tx/Rx units, forexample. Alternatively, an example depth sensor such as a LiDAR sensor,a time of flight (ToF) camera, or an acoustic sensor can be used inplace of the radar unit, for example. The radar unit 204 sensor capturesscene information that includes distances of objects from the imagingsystem 200. In an example system, a depth sensor includes a radar unitthat captures radar information that can include radar metadata that isindicative of one or more physical relationships between an objectswithin a scene detected by the radar unit and the imaging system 200,such as distance, relative speed and angle. A radar data pipeline block210 processes captured radar data and corresponding tracking informationand corresponding classification information to extract radar ROIs thatcorrespond to images of 3D world objects within a scene in a sequence ofradar frames. A radar data buffer block 212 stages a sequence of radardata frames together with corresponding tracking and classificationinformation for use in geometric calibration processing.

The camera system 202 and the radar unit can be geometricallyco-registered. An ROI filter block 214 can filter image ROIs and canfilter radar ROIs used in adjusting geometric parameters (also referredto as ‘extrinsic’ parameters) during camera calibration. An examplefilter block 214 can filter based upon one or more of threshold numberof classified ROIs, classification confidence levels, and distance lossas between image ROIs and radar ROIs, for example. An ROI matching block216 matches image ROIs and radar ROIs that correspond to a common 3Dworld object, such as a person, dog, or car, for example to determineco-registered ROI pairs. A calibration block 218 adjusts the camerasystem's geometric parameters based upon co-registered ROI pairs andcorresponding depth sensor metadata to calibrate the camera system.

FIG. 2B is an illustrative drawing representing location of first andsecond image ROIs 232, 234 identified using the camera pipeline block206 within a camera field of view 230 of the imaging system 200 of FIG.2. FIG. 2C is an illustrative drawing representing location of first andsecond radar ROIs 252, 254 identified using the radar pipeline block 210within a radar field of view 230 of the imaging system 200 of FIG. 2.

Referring to FIG. 2B, an example camera field of view 230 has aZ_(C)-axis and an X_(C)-axis. The camera pipeline block 206 can identifythe example first image ROI 232 and can determine corresponding firstZ_(C)-axis extent E_(IZ1), first X_(C)-axis extent E_(IX1), and firstimage center location C_(I1). The camera pipeline block 206 can identifythe example second image ROI 234 and can determine corresponding secondZ_(C)-axis extent E_(IZ2), second X_(C)-axis extent E_(IX2), secondimage center location C_(I2). The center locations C_(I1), C_(I2), canbe determined based upon determining median locations within the imageROIs 232, 234 or other centering metrics, for example. The pipelineblock 206 also can determine classification and tracking of the firstand second image ROIs 232, 234.

Referring to FIG. 2C, an example radar field of view 250 has aY_(R)-axis and an X_(R)-axis. The camera pipeline block 206 can identifythe example first image ROI 252 and can determine corresponding firstY_(R)-axis extent E_(RY1), first X_(R)-axis extent E_(RX1), and firstradar center location C_(R1). The camera pipeline block 206 can identifythe example second radar ROI 254 and can determine corresponding secondY_(R)-axis extent E_(RY2), second X_(R)-axis extent E_(RX2), and secondimage center location C_(R2). The pipeline block 206 also can determineclassification and tracking of the first and second radar ROIs 252, 254.

An example ROI matching block 216 can be configured to match image ROIsand radar ROIs based upon the X_(C) in the camera field of view (FOV)230 and X_(R) in the radar FOV since the camera system 202 and the radarunit 204 can be aligned in their respective X_(C) and X_(R) axes.Matching based upon X_(C) and X_(R) axes can be most suitable in sparsesense with fewer identified objects.

An example ROI matching block 216 can be configured to match image ROIsand radar ROIs based upon other data such as matching track histories ofimage ROIs and radar ROIs (e.g., direction, path of travel), matchingclassification (e.g., person, car, pet), matching extent (e.g., e.g.,match image ROI extent with a corresponding radar ROI extent).

The calibration block 218 adjusts calibration based uponradar-determined distance. For example, assuming that the ROI matchingblock 216 co-registers image ROI 232 with radar ROI 252, and the radarunit 204 determines a distance distance=Y_(R1) between the radar unit204 and the center location C_(R1) of radar ROI 250, then a center pointC_(I1) can be set as the radar-determined distance Y_(R1). Similarly,assuming that the ROI matching block 216 co-registers image ROI 234 withradar ROI 254, and the radar unit 204 determines a distancedistance=Y_(R2) between the radar unit 204 and the center locationC_(R2) of radar ROI 254, then a center point C_(I2) can be set at theradar-determined distance Y_(R2). A plurality of radar-determineddistances can be used as explained more fully below with reference toTables 1, 2, 3 to determine best fit estimates of photometricparameters.

In an alternative example imaging system 200, radar unit 204 can provideradar information using spherical coordinates e.g., (range, θ, φ), whichcan be mapped to cartesian coordinates (x, y, z). Moreover, in analternative example imaging system 200, a radar unit 204 can tack acluster of locations at a radar ROI and use a median range of an area oflocations on the radar ROI as a radar distance.

Camera Model Example

This section describes a known pinhole camera model to provide anillustrative general example of the kinds of geometric parameters thatcan require adjustment during calibration of the example imaging systemsdescribed herein.

FIG. 3 is an illustrative drawing representing an example known pinholecamera model 300. A 2D image is formed in an image plane 302 torepresent a 3D world object. A 3D point P=(X, Y, Z) on the 3D real worldimage object 306 corresponds to a 2D point (u, v) in the image plane.The example pinhole camera model 300 includes a camera center located atFc. An optical axis Zc, also referred to as a principle axis, extendsthrough a principal point (cx, cy) of the image plane. Images from oneor more cameras can be used to analyze spatial relationships of a 3Dworld scene, such as the size or position of objects relative to anexample camera and relative to each other. Using a pinhole cameraapproximation, for example, a mapping of positions in a 3D world sceneand their locations in an image plane is governed by the followingexample projection transformation expression.

S[u v 1]=[f _(x) γc _(x) 0 f _(y) c _(y) 0 0 1][r ₁₁ r ₁₂ r ₁₃ t ₁ r ₂₁r ₂₂ r ₂₃ t ₂ r ₃₁ r ₃₂ r ₃₃ t ₃][X Y Z 1]  (3)

where (u, v) represents a pixel location in the image plane and (X, Y,Z) is a corresponding location in a 3D world. An intrinsic (photometric)parameter matrix (referred to herein as ‘K’) includes: fx, fy whichrepresent focal length in the x-axis and the y-axis respectively; (cx,cy), which represent coordinates of the principal point; and γ, whichdescribes the relative orthogonality of the image axes. To simplify theexplanation, γ=0 is assumed, which is typical for digital cameras. Angeometric parameter matrix (referred to herein as ‘[R t]’) includesrotation parameters r₁₁-r₃₃ and translation parameters, t₁-t₃. Thegeometric parameters can represent camera pose in six degrees offreedom: three translation axis directions along the, x-axis, y-axis,and z-axis; and three angular rotation directions about the, x-axis,y-axis, and z-axis. The value S represents a scale factor used to denoteeither fx or fy as determined by context.

The projection transformation expression (1) can be represented in asimplified from as,

S*p _(c) =K[R t]P _(w)  (4)

Where p_(c) is coordinate location of an image projection in the cameraimage plane: p_(c)=(x, y, I)^(T) and P_(w) is a 3D world location ofcorresponding object: P_(w)=(X, Y, Z, I)^(T)

Camera pose must be properly calibrated to successfully infer spatialrelations within a 3D world based upon images formed on the camera imageplane. Assuming that intrinsic (photometric) parameters are constant,calibration involves adjustment of geometric parameters within theexample geometric parameter matrices camera projection matrix, [R t].

Single Camera System with Depth Sensor-Based Camera Calibration

FIG. 4 is an illustrative drawing showing an example simplified diagramof a single camera system 400 with depth-sensor based camera calibrationthat can be implemented using the example imaging system architecture200 of FIG. 2A. The example single camera system 400 includes a camera402 and a includes a radar-based depth sensor 404. The single camera(also referred to as a mono-camera) 402 and the radar unit 404 have aknown fixed geometric relationship between them; distance between themand angular rotation relationships between their fields of view areknown and fixed. In an example system 400, the camera and the radar unit404 are geometrically co-registered: the camera 402 can be calibrated toestimate 3D coordinates of 3D world objects appearing in a scene and mayhave the same 3D frame of reference as the radar unit 404. Thus, thereis a known mathematical transform relationship that can be used totransform between the camera coordinates and radar coordinates. In anexample system, the camera 402 and the radar unit 404 can be located ona single circuit module.

An example mono-camera 402 is associated with geometric parametersmeasured relative to a ground plane. The mono-camera 402 is positionedrelative to an (x, y, z) coordinate system in which an (x, y) plane islocated in a ground plane and a z-axis extends through the camera, whichis located at a height (H) above the (x, y) ground plane. Geometricparameters of the example mono-camera include rotational parameters Rrelative to the ground plane (e.g., parallel to the x-axis, y-axis, andz-axis) and H.

FIG. 5 is an illustrative flow diagram representing an example process500 to calibrate pose of the camera 402. The process 500 can run usinginstructions stored in one or more storage devices to configure one ormore processing devices of the imaging system architecture 200.Referring to FIG. 5, step 502 captures image data from the camera FOV atan image plane of the camera. Step 504 can use a trained machinelearning model to detect image regions of interest (ROIs) within theimage data. Step 506 can use a trained machine learning engine toclassify the image ROIs based upon 3D world objects corresponding to theimage ROIs. Step 508 captures radar data from the radar FOV. Step 510detects radar ROIs within the radar data. Step 512 determines radardistances from 3D world objects to associate with the radar ROIs. Step514 can use a trained machine learning engine to classify the radar ROIsbased upon 3D world objects corresponding the radar ROIs. Step 516matches image ROIs and radar ROIs that correspond to common 3D worldobjects to determine co-registered ROI pairs. In particular, an examplestep 516 can match by tracking a motion pattern of both ROIs andassociate them based on the trajectory similarity. An example step 516can minimize a euclidean distance of both ROI's after projecting theradar ROI to the image plane. In another example, step 516 can match byevaluating classifications associated with image ROIs and radar ROIs toidentify image ROIs and radar ROIs that share a 3D world objects incommon. Step 518 adjusts geometric parameters based upon co-registeredROI pairs and corresponding radar-determined distances. In an examplegeometric parameter adjustment process, a generalized least-squares fitprocess is used to adjust geometric parameters used in projectionsidentified based upon co-registered ROI pairs and correspondingradar-determined 3D world distances.

The following Table 1 sets forth a set of example projections that arebased on equation (4).

TABLE 1 S*((u₁, v_(1, 1))^(T) =K_(m)[R t]_(m) * (X₁,Y₁, Z_(1R), 1)^(T)S*((u₂, v_(2, 1))^(T) =K_(m)[R t]_(m) * (X₂,Y₂, Z_(2R), 1)^(T) S*((u₃,v_(3, 1))^(T) =K_(m)[R t]_(m)* (X₃,Y₃, Z_(3R), 1)^(T) ... S*((u_(n),v_(n, 1))^(T) =K_(m)[R t]_(m) * (X_(n),Y_(n), Z_(nR), 1)^(T)

Table 1 shows multiple example projections each involving an geometricparameter matrix [R t]_(m) and an intrinsic (photometric) parametermatrix K_(m) for the example mono-camera of FIG. 4. The geometricparameter matrix includes rotation parameters and a translationparameter, H. In a single camera embodiment, the translation parameter,T, may only include height above a ground plane. The intrinsic parametermatrix K_(m) includes intrinsic parameters that are assumed to beconstant. The fit process adjusts geometric parameters to fit the aboveexample image projections. Specifically, for example, the geometricparameters are adjusted to fit the projection between (u₁, v₁) and(X₁,Y₁,Z_(1R)), which both correspond to a first common 3D world object;to fit the projection between (u₂, v₂) and (X₂,Y₂, Z_(2R)), which bothcorrespond to a 10 second common 3D world object; to fit the projectionbetween (u₃, v₃) and (X₃,Y₃, Z_(3R)), which both correspond to a thirdcommon 3D world object; and to fit the projection between (u_(n), v_(n))and (X_(n), Y_(n), Z_(nR)), which both correspond to an nth common 3Dworld object Adjustment of the geometric parameters can start from aninitial pose setting such as initial height and angle estimates that canbe ones measured during device installation, for example.

It will be assumed that the camera image plane and the radar unit are,in effect, at equal ranges, that is at equal distance magnitude, fromrespective 3D world objects since the distances from such objects areorders of magnitude greater than a distance between the camera andradar. The inventors realized that each radar-determined distanceZ_(1R), Z_(2R), Z_(3R) and Z_(nR) in the example image projections,therefore, represents an actual distance between the camera and arespective 3D world point and that the determined distances can be usedto determine a scaling factor S used in the projection equation. A scalefactor S normalized to a unit range can be determined using a depthsensor-determined distance based upon the following relationship,

S=1/f−d _(pixel) /Z _(R)  (5)

where f is focal length, Z_(R) is depth sensor-determined distancebetween an ideal lens plane and a 3D world object and d_(pixel) is adimension of the object in the camera image plane, measured in pixels.

A map image ROI step 520 uses the adjusted geometric parametersdetermined at step 518 to map co-registered image ROI coordinates fromthe camera (u, v) plane to a 3D world X, Z space. A decision step 522determines whether differences between the mapped image ROI coordinatesand corresponding coordinates of co-registered radar ROIs are within anacceptable error range. In an example imaging system 200, an exampleacceptable error range is within 0-2 pixels. In an example imagingsystem 200, an error range is selected such that a predefined costfunction for matching ROIs is close to zero. In response to adetermination that the difference is not acceptable, control to step502. In response to a determination that the difference is acceptable,step 524 updates the camera using the adjusted geometric parameters tocalibrate the camera to accurately infer spatial relationships in thecamera FOV. In response to a determination that the difference is notacceptable, step 526 returns control to step 502.

Multiple Camera System with Depth-Sensor Based Camera Calibration FIG.6A is an illustrative example simplified diagram of a multi-camerasystem 600 with depth-sensor based camera calibration that can beimplemented using the imaging system architecture 200. The examplemulti-camera system includes a first camera 602 and a second camera 604,and a radar depth sensor 606. The first camera 602 acts as a referencecamera that has a fixed geometric relationship with the radar unit 606;distance between them and relative angular rotation between their fieldsof view are known and fixed. Moreover, the first (reference) camera 602and the radar unit 606 are geometrically co-registered: the first camera602 is calibrated to estimate the 3D coordinates of objects appearing ina scene and can have the same 3D frame of reference as the radar unit606. For example, the first camera 602 and the radar unit 606 can belocated on a single circuit module.

FIG. 6B is an illustrative example drawing showing regions of interestin the first and second camera and in the radar fields of view. Morespecifically, the first camera 602 includes a first image plane 608. Thesecond camera includes a second image plane 610. The radar unit 606includes a radar field of view 612.

Referring to FIGS. 6A-6B, in operation, multiple respective 3D worldobjects O₁, O₂, O₃, and O_(n) are within respective FOVs of the firstcamera 602, and the second camera 604, and the radar unit 606. The firstcamera 602 captures respective object image information at coordinates(u_(A1), v_(A1)), (u_(A2), v_(A2)), (u_(A3), v_(A3)), and (u_(An),v_(An)) coordinates within the first image plane 608 corresponding tothe respective objects). The second camera 604 captures object imageinformation at (u_(B1), v_(B1)), (u_(B2), v_(B2)), (u_(B3), v_(B3)), and(u_(Bn), v_(Bn)) coordinates within the second image plane 910. Theradar unit 606 captures radar information indicating target regions ofinterest, R_(T1), R_(T2), R_(T3), and R_(Tn) within the radar field ofview 612 The radar information includes radar metadata, such asdistance, speed, trajectory, and angle, for example.

FIG. 7 is an illustrative flow diagram representing an example process700 to calibrate the first and second cameras 602, 604. The process 700can run using instructions stored in one or more storage devices toconfigure one or more processing devices of the imaging systemarchitecture 200. Step 702 captures first image data by the first camera602 and captures second image data by the second camera 604. Step 704detects image ROIs within the first and second image data of the firstand second cameras. Step 706 uses a first trained machine learningengine to classify image ROIs within first image data of the firstcamera 602 based upon 3D world objects corresponding to the image ROIs.Step 708 captures radar data by the radar unit 606 from the radar dataFOV 612. Step 710 detects radar ROIs within the radar data. Step 712determines radar distances to associate with the radar ROIs. Step 714uses a second trained machine learning engine to classify the radar ROIsbased upon 3D world objects corresponding to the radar ROIs. Step 716matches image ROIs captured by the first camera 602 with radar ROIs thatcorrespond to common 3D world objects to determine co-registered ROIpairs.

Step 718 adjusts geometric parameters of the camera system based uponco-registered ROI pairs and associated radar-determined 3D worlddistances. More specifically, in step 718 an example first fit processuses a generalized least-squares fit process, for example, to adjustfirst geometric parameters represented by a first matrix function M1associated with the first camera 602, based upon projections identifiedusing co-registered ROI pairs and corresponding radar-determined 3Dworld distances. Next, an example second fit process uses a generalizedleast-squares fit process, for example, to adjust second geometricparameters represented by a second matrix function M2 associated withthe second camera 604, based upon the adjusted first geometricparameters M1.

An example first transformation matrix function M1 can include one ormore matrices that include intrinsic (photometric) parameters andgeometric parameters of first camera having scale S, to transformcoordinates (u_(A), v_(A)) in a camera plane of first camera, to 3Dworld coordinates (X, Y, Z).

S*(u _(A) ,v _(A))*M1=(X,Y,Z)  (6)

An example second transformation matrix function M2 includes one or morematrices that include intrinsic parameters and geometric parameters ofsecond camera having scale S, to transform coordinates (u_(B), v_(B)) ina camera plane of second camera, to coordinates (X, Y, Z).

S*(u _(B) ,v _(B))*M2=(X,Y,Z)  (7)

In an example imaging system, scale S is the same for both the first andsecond cameras, although an alternative example imaging system,different cameras can have different scales. An example transformationbetween first camera coordinates and second camera coordinates can bedetermined as follows,

(u _(B) ,v _(B))=((u _(A) ,v _(A))*M1)*inv(M2)  (8)

The following Table 2 sets forth a set of example projections usedduring the example first fit process based on equation (6).

TABLE 2  (u_(A1),v_(A1))*M1 = (X₁, Y₁, Z_(1R))  (u_(A2),v_(A2))*M1 =(X₂, Y₂, Z_(2R))  (u_(A3),v_(A3))*M1 = (X₃, Y₃, Z_(3R)) ... (u_(An),v_(An))*M1 = (X_(n), Y_(n), Z_(nR))

Table 2 shows multiple example projections each involving the firsttransformation matrix function M1. The first example fit process adjustsgeometric parameters within M1 to fit the above example imageprojections. Specifically, for example, the geometric parameters of M1are adjusted to fit the projection between (u_(A1),v_(A1)) and(X₁,Y₁,Z_(1R)), which both correspond to a first common 3D world object;to fit the projection between *(u_(A2),v_(A2)) and (X₂,Y₂,Z_(2R)), whichboth correspond to a second common 3D world object; to fit theprojection between (u_(A3),v_(A3)), which both correspond to a thirdcommon 3D world object and (X₃,Y₃, Z_(3R)); and to fit the projectionbetween (u_(An),v_(An)) and (X_(n),Y_(n),Z_(nR)), which both correspondto an nth common 3D world object. Adjustment of the geometric parameterscan start from an initial pose setting such as an initial estimate onheight and angle could be measure during setup, for example.

The following Table 3 sets forth a set of example transformations usedduring the example second fit process based on equations (6), (7), (8).

TABLE 3 (u_(B1), v_(B1)) = ((u_(A1), v_(A1))*M1) * inv(M2) (u_(B2),v_(B2)) = ( (u_(A2), v_(A2))*M1) * inv(M2) (u_(B3), v_(B3)) = ( (u3₁,v_(A3))*M1) * inv(M2) ... (u_(Bn), v_(Bn)) = ( (u_(An), v_(An))*M1) *inv(M2)

Table 3 shows multiple example projections each involving the firsttransformation matrix function M1 and the second transformation matrixfunction M2. The example second fit process uses the adjusted matrix M1to adjust geometric parameters of M2. Specifically, for example, thegeometric parameters of M2 are adjusted to fit the transformationbetween (u_(B1), v_(B1)) and (u_(A1),v_(A1)); to fit the transformationbetween (u_(B2),v_(B2)) and (u_(A2),v_(A2)); to fit the transformationbetween (u_(B3),v_(B3)) and (u_(A3),v_(A3)); and to fit thetransformation between (u_(Bn), v_(Bn)) and (u_(An),v_(An)). Adjustmentof the geometric parameters can start from an initial pose setting suchas an initial estimate on height and angle could be measure duringsetup, for example.

First camera can be calibrated with adjusted matrix function M1. Secondcamera can be calibrated with adjusted matrix function M2. Thecalibrated cameras can be used together to infer spatial relationshipswith objects in the 3D world.

Imaging System with Depth Sensor-Based Single Camera Calibration BasedUpon Moving Objects

FIG. 8 is an illustrative schematic block diagram of an example imagingsystem 800 with radar-based single camera calibration based upon movingobjects. The imaging system 800 includes a camera 802, a radar unit 804,and a computing machine described below with reference to FIG. 12,including one or more processor devices configured to operatively coupleto one or more non-transitory storage devices that store instructions toconfigure the processing devices to perform the block functionsdescribed as follows. The camera 802 captures 2D image information at acamera image plane (not shown) from a 3D world scene within a camerafield of view (FOV). A frame buffer block 806 saves the camera imageinformation in a sequence of camera image frames. An image datapreprocessing stream block 808 preprocesses the image frames to producevisual images. A motion detection block 810 detects object motion toidentify image ROIs within the image information. An example motiondetection block 810 can use an optical flow algorithm to detect imagemotion. An image classification block 812 classifies image ROIs into oneof several category values (for example, is this object a person, ananimal, a vehicle, etc.) and produces corresponding image classificationconfidence scores. An example image classification block 812 can usedeep learning CNN-based algorithms to perform classification. Moreparticularly, an example classification block can use deep learningCNN-based algorithms such as Single Shot Detector that can performdetection and classification at the same time. Multiple moving objectsmay be identified and classified based upon image information capturedwithin a camera FOV. An image tracking block 814 tracks classified imageROIs corresponding to objects within the camera FOV, over time. Anexample image tracking block 814 tracks image ROIs that correspond toobject images. More particularly, an example image tracking block 814tracks for each moving object image: a corresponding image ROI; anobject image track identifier (image track ID); and an object imageclass and associated confidence score.

The radar unit 804 captures radar information within a radar FOV. Aradar data buffer block 816 saves the radar data in a sequence of radarframes. A radar data preprocessing block 818 preprocesses the radarframes to produce radar metadata, such as distance, speed, trajectory,and angle, for example. Radar preprocessing may include computing arange, a velocity, or an angle of the moving entity using a fast Fouriertransform (FFT), for example. A radar ROI detection block 820 detectsradar ROIs within the captured radar information that correspond to 3Dworld objects within the radar FOV. A radar ROI classification block 822classifies moving objects corresponding to detected radar ROIs into oneof several category values (e.g., person, animal, vehicle, etc.) andproduces corresponding radar classification confidence scores. Morespecifically, an example ROI classification block 822 uses Deep learningmodels based on Recurrent neural networks to classify radar ROIs.Multiple moving objects may be identified and classified based uponradar information captured within the radar FOV. A radar tracking block824 tracks radar ROIs over time. An example radar tracking block 824tracks multiple moving radar ROIs. More particularly, an example radartracking block 824 tracks for each radar target object: a correspondingradar ROI; a radar object track identifier (radar track IDs); radarobject metadata (e.g., radar depth): and radar object class andassociated confidence score.

A first filter block 826 conditions adjustment of geometric parametersupon a threshold count of co-registered ROI pairs. An example firstfilter block 826 can delay calibration in response to too manyco-registered ROI pairs being tracked because with too many objects,there can be a higher likelihood of error in co-registration. An examplethreshold count can be selected based upon how far these objects arefrom each other; the closer the objects, the lower the count.Alternatively, a threshold count can be determined based upon dependshow sparse the scene is; the sparser the scene, the higher the count. Ina crowded scene, for example, with many objects in the background of thescene, greater care can be required with image reflections, and it maybe necessary to limit to matching one moving object at a time. In a lesscrowded scene, for example, it can be possible to match two or threemoving objects at a time if they are far enough from each other.

A second filter block 828 conditions adjustment of geometric parametersupon co-registered ROI pairs meeting a threshold confidence level. Anexample second filter block 828 can prevent use of co-registered ROIpairs that do not meet the threshold confidence level. In an exampleimaging system 800, a threshold confidence level is selected tominimizes a false positive rate. For example, in an example system 800,a confidence level greater than 0.8 (>0.8), in a 0-1 confidence scale,can be used. In an example system, confidence is a measure of Euclideandistance between ROIs. Loss is low when the two ROIs are close in thisEuclidean space.

A third filter block 830 determines whether co-registered ROIs meet aloss threshold. More particularly, an example third filter block 830 candetermine whether a z-distance measured using the camera 802 for animage ROI and a z-distance measured using the radar unit 804 for a radarROI registered to the image ROI meet a distance loss threshold. Anexample third filter block 830 also determines whether trackingtrajectory consistency as between image ROIs and radar ROIs ofco-registered ROI pairs meet a prescribed tracking loss threshold. In anexample imaging system 800, a loss threshold can be determined basedupon a predefined distance resolution. An example third filter block 830can have a distance resolution for z-direction and tracking that is in arange of a few centimeters, for instance. Co-registered ROI pairs thatdo not meet the loss threshold requirements are excluded from use duringadjustment of geometric parameters.

Depth assignment block 834 associates radar-determined depths toco-registered ROIs pairs that meet the loss threshold. Moreparticularly, block 834 assigns to a co-registered ROI pair, aradar-determined depth associated with the radar ROI of the pair. Aninitialization block 836 sets an initial estimate of the camerageometric pose parameters, e.g., height and rotation. Calibration block838 produces adjusted geometric parameters based upon co-registeredimage/radar ROIs and corresponding radar metadata. An examplecalibration block 838 can adjust geometric parameters according to theprojection fitting process described with reference to Table 1. A cameraupdate block 840 provides the adjusted geometric parameters to thecamera 802.

Imaging System with Depth Sensor-Based Multi-Camera Calibration BasedUpon Moving Object

FIG. 9 is an illustrative schematic block diagram of an example imagingsystem 900 with radar-based multi-camera calibration based upon movingobjects. The imaging system 900 includes multiple cameras 902-1, 902-2,. . . 902-n, a radar unit 904, and a computing machine described belowwith reference to FIG. 12, including one or more processor devicesconfigured to operatively couple to one or more non-transitory storagedevices that store instructions to configure the processing devices toperform the block functions described as follows. Each camera 902-1,902-2, . . . 902-n captures image information at a corresponding cameraplane (not shown). A frame buffer block 906 saves respective cameraimage information for each camera in a respective sequence of cameraimage frames. An image data preprocessing stream block 908 preprocessesthe image frames to produce image metadata. A motion detection block 910detects object motion to identify image (ROIs) within the captured imageinformation. An image classification block 912 classifies image ROIsinto one of several category values and produces corresponding imageclassification confidence scores. An example image classification block912 can use deep learning CNN-based algorithms to performclassification. More particularly, an example classification block canuse deep learning CNN-based algorithms such as Single Shot Detector thatcan perform detection and classification at the same time. Multiplemoving objects may be identified and classified based upon imageinformation captured within the multiple camera FOVs. An image trackingblock 914 tracks image classified image ROIs corresponding to objectswithin camera FOVs, over time. More particularly, an example imagetracking block 914 tracks for each object image: a corresponding imageROI; an image object track identifier (image track ID); and an imageobject class and corresponding confidence score.

The example imaging system 900 further includes a radar unit 904 tocapture radar data, a radar data buffer block 916, a radar datapreprocessing block 918, a radar ROI detection block 920, a radar ROIclassification block 922, and a radar tracking block 924, which aresimilar to those described above with reference to FIG. 8 and theimaging system 800, and which will be understood by persons skilled inthe art from the above description. The image system 900 also includes afirst filter block 926, a second filter block 928, an ROI matching block930, and a third filter block 932, which are similar to those describedabove with reference to the imaging system 800. For economy ofdescription, blocks in the imaging system 900 that are similar tocorresponding blocks in the imaging system 800 are not described again.

A calibration block 934 to produces adjusted geometric parameters basedupon co-registered image/radar ROIs and corresponding radar metadata. Anexample calibration block 934 can adjust geometric parameters accordingto a projection fitting process and a transformation process similar tothose described with reference to Tables 2 and 3, respectively. Thecalibration block provides the adjusted geometric parameters to themultiple camera 902-1 to 902-n.

Imaging System with Depth Sensor-Based Single Camera Calibration BasedUpon Static Objects

FIG. 10 is an illustrative schematic block diagram of an example imagingsystem 1000 with radar-based single camera calibration based upon staticobjects. For economy of description, blocks in the imaging system 1000that are similar to corresponding blocks in the imaging system 800 ofFIG. 8 are not described again. A background segmentation block 1050performs semantic segmentation to assign a classification to each pixelwithin the sequence of image frames. More specifically, a class isassigned to each pixel in the captured image information. An objectdetection and classification block 1052 detects individual ROIs withinthe classified pixel information. An example image classification block1052 can use deep learning CNN-based algorithms to performclassification. More particularly, an example classification block canuse deep learning CNN-based algorithms such as Single Shot Detector thatcan perform detection and classification at the same time. For eachclassified ROI, the object detection and classification block 1052provides to the first filter 1026 a corresponding image ROI, an objectimage class, and associated confidence score. For each classified ROI,the radar ROI classification block 1022 provides to the first filter1026 a corresponding image ROI, a distance value per radar ROI, anobject image class, and an associated confidence score.

Imaging System with Depth Sensor-Based Multi-Camera Calibration BasedUpon Static Objects

FIG. 11 is an illustrative schematic block diagram of an imaging system1100 with radar-based multi-camera calibration based upon staticobjects. For economy of description, the individual blocks of theimaging system 1100 are not described again. It is noted that blocks ofthe fifth system correspond to blocks of the imaging system 900 of FIG.9 except for a background segmentation block 1150 and an objectdetection block 1152, which correspond to similar blocks in the imagingsystem 1000 of FIG. 10. Thus, persons skilled in the art will understandthe imaging system 1100 based upon the above descriptions of the imagingsystems 900 and 1100.

Calibration of Photomertric Camera Parameters

FIG. 12A is an illustrative drawing showing an example imaging system1200 with depth sensor-based camera calibration that includes a sharedfield of view 1301 of a camera 1202 and a radar unit 1204, in accordancewith some embodiments. As shown, both the camera 1202 and the radar unit1204 observe the field of view 1201 at the same time. The camera 1202and the radar unit 1204 can be geometrically aligned to share a commonframe of reference as described above with reference to the embodimentsof FIG. 2, for example. A 3D world object 1205 is visible within the FOV1201 to both the camera 1202 and the radar unit 1204. FIG. 12B is anillustrative drawing showing a camera-detected object view 1205Cvisible, a camera view 1207 and a radar-detected object view 1205Rvisible within a camera view 1209.

FIG. 13 is an illustrative schematic block diagram showing additionaldetails of the imaging system 1200 of FIG. 12A. The imaging system 1200includes the camera system 1202, and the radar unit 1204, and a computersystem, including one or more processor devices configured tooperatively couple to one or more non-transitory storage devices thatstore instructions to configure the processing devices to perform theblock functions described as follows. An example camera system 1202operates as an image frame processing pipeline that processes a sequenceof image data frames.

An example camera system 1202 includes a camera image capture unit 1303that includes a lens system 1306 to adjust focus based upon objectswithin the FOV 1201 and an image sensor 1308 to capture image data forobjects within the FOV 1201. An image sensor 1308 can include a CCDsensor array or can include a CMOS sensor array, for example. The camerasystem 1202 also includes an image frame buffer 1309, an imageprocessing block 1310, and photometric parameter adjustment block 1312to adjust photometric (intrinsic) camera parameters. An exampleadjustment block 1312 adjusts a group of photometric parameters basedupon luminance determined using the image processing block 1310 and oneor more of object distance, object speed and object classificationdetermined based upon radar information captured by the radar unit 1204.A display block 1311 provides a visual display of a processed imageframe.

The example camera system 1202 operates as an image frame processingpipeline that processes a sequence of image frame data and based uponthe camera's intrinsic parameters. In the course of processing a currentimage frame in the sequence, one or more of the intrinsic parameters canbe adjusted based upon luminance, radar-determined object distance,radar-determined object speed, and radar-detected object classificationassociated with the current frame such that a subsequent image frame inthe sequence is processed using the adjusted intrinsic parameters.Adjusted intrinsic parameters are used in the processing of a subsequentimage data frame in the sequence of image data frames. Morespecifically, for example, certain intrinsic parameters can be providedto the camera lens system 1306 to adjust focal length and to the cameraimage sensor 1308 to determine exposure time. Moreover, for example,certain intrinsic parameters can be provided to the image processingblock 1310 to determine automatic white balance (AWB), high dynamicrange (HDR) and tone map.

An example radar unit 1204 includes a radar sensor 1330 that can havesingle transmit/receive (Tx/Rx) units or operate as phased array or MIMOwith multiple Tx/Rx units, for example, to capture radar informationwithin the FOV 1201. A radar data buffer block 1326 saves the radar datain a sequence of radar frames. An example radar data preprocessing block1328 preprocesses the radar frames using a fast Fourier transform (FFT)to produce radar metadata, such as distance, speed, trajectory, andangle, of the radar-detected object view (a ‘radar object’) 1205R. Aradar ROI detection block 1330 detects radar ROIs within the capturedradar information that correspond to 3D world objects within the sharedFOV 1201. A radar ROI classification block 1332 classifies radar objectscorresponding to detected radar ROIs into one of several category values(e.g., person, animal, vehicle, etc.) and produces corresponding radarclassification confidence scores. Multiple radar objects may beidentified and classified based upon radar information captured withinthe FOV 1201. A radar tracking block 1334 tracks radar ROIs over time.An example radar tracking block 1334 tracks multiple moving radar ROIs.More particularly, an example radar tracking block 1334 tracks for eachradar object: a corresponding radar ROI; a radar object track identifier(radar track IDs); object depth per track ID, object speed per track IDand object classification associated confidence score per track ID.

An example first filter block 1336 conditions use of radar-basedinformation to determine adjustment of intrinsic parameters upon athreshold count of tracked radar objects. An example first filter block1336 can delay calibration in response to too many tracked objects beingtracked because too many objects can increase the chance of error. Morespecifically, an example threshold count can be selected to minimize thechance of error. An example second filter block 1338 conditions use ofradar-based information to determine adjustment of intrinsic parametersupon tracked radar objects meeting a threshold confidence level. Moreparticularly, an example second filter block 1338 can prevent use ofradar objects that do not meet a threshold confidence level based uponIn an example imaging system 1300, a threshold confidence level isselected to minimizes a false positive rate. For example, in an examplesystem 1300, a confidence level greater than 0.8 (>0.8), in a 0-1confidence scale, can be used. In an example system, confidence is ameasure of Euclidean distance between ROIs. Loss is low when the twoROIs are close in this Euclidean space. In response to one or both ofthe first filter block 1336 and the second filter block 1338 preventinguse of certain radar-based information for intrinsic parameteradjustment, an example intrinsic parameter adjustment block 1312 canadjust values of a group of camera intrinsic parameters including 3A(AE, FA, AWB), gain, and HDR, for example, based upon luminance andbased upon distance information determined using an alternative knowndistance measurement system, for example.

Referring again to FIGS. 12A-12B, the imaging system 1300 of FIG. 13 canbe configured as described above with reference to FIG. 2, for example,to match region of interest (ROI) pairs within the camera image 1207 andwithin the radar image 1209. More specifically, the imaging system 1300can be configured to match a camera-detected view of an object 1205Cwith radar-detected view of the object 1205R. In operation, an examplecamera system 1202 can select a camera-detected object within the FOV1201 as a camera target object. Targets can be selected targets ofinterest such as human, animal, car that have contextual significance ina scene. For example, the camera system 1202 can identify an object inmotion as described above with reference to block 810 of FIG. 8, forexample, and can select the identified object in motion to be act ascamera target object. The radar unit 1204 can classify and track an ROIcorresponding to the camera target object as explained with reference toblocks 1330, 132, 1334 of FIG. 13. The camera system 1202 also caninclude blocks (not shown) to classify and track the camera targetobject as described with reference to blocks 812 and 814 of FIG. 8, forexample. The image system 1300 can be configured with blocks (not shown)to match radar ROIs and image ROIs in co-registered ROI pairs asdescribed with reference to block 830, for example. The camera system1202 can be configured to associate radar-determined speed andradar-determined distance with camera target object based upondetermining a co-registered ROI pair for which the camera target objectcorresponds to the image ROI; speed and distance associated with theradar ROI of that co-registered ROI pair are associated with the cameratarget object. The adjustment block 1312 can use that radar-detecteddistance and radar-detected speed to adjust the intrinsic parameters.

An example camera system 1202 can include a known supplementary distancemeasurement system (not shown), such as IR, sonic TOF, or Lidar tomeasure distances from a camera-detected objects 1205C (‘cameraobjects’) within the camera view 1207, for example. Thus, a knowndistance system can be used in addition to or in place of the radar unit1204 to determine object distance.

FIG. 14 is an illustrative block diagram showing certain additionaldetails of an example camera system 1402 in accordance with someembodiments. The camera system 1402 includes a camera image capture unit1403. The camera system also includes an image processing block 1410 andan intrinsic parameter adjustment block 1412 that can include a computersystem, comprising one or more processor devices configured tooperatively couple to one or more non-transitory storage devices thatstore instructions to configure the processing devices to perform imageprocessing and intrinsic parameter adjustment as described below. Anexample camera image capture unit 1403 includes a lens system 1406 animage sensor 1408 that can include a pixel sensor array to capture asequence of image data frames. The camera system 1402 also includes animage frame buffer 1409 to save a sequence of received input image dataframes 1411. An example image processing block 1410 includes a luminanceblock 1440, a white balance block 1442, and an HDR block 1445 andassociated tone mapping block 1446. An example luminance block 1440 usesknown techniques to determine image luminance based upon received inputimage frame data. The determined luminance indicates light intensityincident upon a pixel sensor array of the image sensor 1408. An exampleluminance block 1440 can produce a luminance histogram, for example, toindicate light intensity distribution across a pixel sensor array theimage sensor 1408. An example white balance block 1442 uses knowntechniques to estimate color within of a light source, e.g., lightreflected from an object within the FOV 1201, and to adjust whitebalance within a received image data frame based upon an estimateddominant illuminant color within the image data. An example HDR block1444 uses known techniques to render images resulting from mergingmultiple lower dynamic-range or standard-dynamic-range images. HDRimages generally can represent a greater range of luminance levels thancan be achieved using more traditional methods. An example tone mappingblock 1446 uses known techniques to compress the extended luminosityrange of HDR images for display on a display device 1411.

An example intrinsic parameter adjustment block 1412 produces controlsignals to adjust intrinsic parameters used to control luminance ofimages captured by the camera system 1402 and to determine camera lensfocal distance, for example. An example adjustment block 1412 receives aluminance information signal from the image processing block 1410 thatindicates luminance of the received image frame and a target luminance.The adjustment block 1412 also can receive object distance, objectspeed, and object classification information from a radar unit, such asfrom radar tracking block 1334 of FIG. 13, for example. An exampleadjustment block 1412 includes a gain control block 1450, anauto-exposure (AE) control block 1452, an auto-focus (AF) control block1454, an AWB (auto-white balance) control block 1456, and an HDR controlblock 1458.

An example gain control block 1450 produces a gain control signal 1451to control image signal gain produced by the image sensor 1408 basedupon a combination of luminance information of a currently receivedframe determined by the luminance block 1440, and one or more ofradar-determined distance, radar-determined speed and radar-determinedclassification corresponding to a selected camera target object. Forexample, if a scene is too dark as measured by total luminance, thenauto-exposure time can be increased to improve brightness.Alternatively, for example, if the scene is too dark as measured bytotal luminance, then gain can be increased to improve brightness.Conversely, if a scene is too bright, then auto-exposure time can bedecreased to improve brightness, or gain can be decreased to improvebrightness. Object speed is a factor used in an example system 1402 todetermine whether to adjust image brightness by adjusting auto-exposureor by adjusting gain, or a combination thereof. If an object is movingat high speed, for example, then increasing auto-exposure integrationtime can lead to motion blur, and consequently, in an example camerasystem 1402, adjusting gain can be the preferred course of action toreduce brightness of a scene that includes a high-speed object.Conversely, if object motion within a scene is slow, for example, thenincreasing gain can increase image noise, and consequently, increasingauto-exposure integration time can be the preferred course of action toincrease brightness of a scene that includes a slow-moving object. So,the example camera system 1402 imposes a compromise or balance betweengain vs auto-exposure, based upon tolerance for motion blur vs tolerancefor image noise.

Moreover, an example gain control block 1450 can adjust the gain controlsignal 1451 based upon one or more of radar-determined classificationfor a selected camera target object to achieve the target luminance. Anexample gain control block AA40 can provide a gain signal 1441 todecrease gain in response to a selected camera target object classifiedas having higher reflectivity such as a car; and conversely, the gaincontrol block AA40 can provide a gain signal 1441 to increase gain inresponse to a selected camera target object classified as having lowerreflectivity such as a person.

An example luminance information signal 1441 provides information todetermine a target luminance for a subsequent image frame. In an examplecamera system 1402, luminance can be computed as mean of the currentluminance channel in an image frame. in YUV color space that is Ychannel, in RGB color space we can compute L=0.5*G+0.25*R+0.25*B. Therecan be many different ways to compute luminance channel from RGB. Inhardware, we can also use Ambinet Light Sensor (ALS), for which we canmeasure scene lux values to determine how dark or bright it is.

An example AE control block 1452 can produce an AE control signal 1453to control image exposure time at the image sensor 1408 based upon acombination of luminance information of a currently received image framedetermined by the luminance block 1440 and one or more ofradar-determined distance, radar-determined speed and radar-determinedclassification of a selected camera target object. For example, Anexample AE block 1452 includes a metering table in which auto-exposurevalues can be chosen based on scene illumination measure by averageluminance or lux. Longer exposure values are associated with darkerscenes and lower exposure values are associated with brighter scenes.The example AE block 1452 balances between illumination, integrationexposure time and gain. An object of an example AE block 1452 is toilluminate a scene by balancing objective of minimizing motion blurwhile limiting image noise.

An example AE control block 1452 can adjust the gain based upon one ormore of radar-determined distance, radar-determined speed, andradar-determined classification for a selected camera target object toachieve the target luminance in a subsequent image frame. An image of afast-moving target object can become blurred within an image frame ifthe exposure time is too long. Therefore, an example AE control block1452 can balance a target luminance goal with a need to reduce targetobject image blur. An example AE control block 1452 can increaseexposure time in response to a more fast-moving selected camera targetobject since shorter exposure time can reduce image blur. Moreover, anexample AE control block 1452 can increase exposure time in response toa more distant selected camera target object since higher luminance maybe required to make enhance its camera image visibility; and conversely,the AE control block 1452 can reduce exposure time in response to aclose-up selected camera target object since lower luminance may berequired to avoid image saturation. Furthermore, an example AE controlblock 1452 can decrease exposure time in response to a selected cameratarget object classified as having higher reflectivity; and conversely,the AE control block 1452 can reduce exposure time in response to aselected camera target object classified as having lower reflectivity.

The AF control block 1454 produces an AF control signal 1455 to controllocation of a focal plane of the lens system 1406. The AWB control block1456 produces an AWB control signal 1457 to adjust processing by thewhite balance block 1442. The white balance block 1442 can modify whitebalance among pixels within a received image frame to make an outputimage produced using the frame to be appear more realistic or genuine tothe human eye, for example. The HDR control block 1458 produces an HDRcontrol signal 1459 to adjust processing by the HDR block 1445. Innormal light conditions, AF, AWB, and HDR control signals can beproduced based upon luminance information signal. However, in someenvironments such as low light environments, there may be insufficientluminance for a camera to detect and track an object within the field ofview 1201. A camera system 1402 can be configured to operate in aradar-control mode in which radar-detected object distance,radar-detected object speed, and radar-determined classification areused to adjust one or more of gain, exposure time, focal length, HDRsettings.

More particularly, an example AF block 1454 provides control signals1455 to a lens actuator (not shown) to cause the lens 1406 to moveposition to bring image objects that are far or close to be in focus. Anexample AF block 1454 is configured to perform a search algorithm toselect a focus point. With range of object of interest known based uponradar information, the AF block 1454 can cause the lens 1406 to move byan amount determined based upon the known distance, to a lens positionselected to bring that object in focus. An example AWB block 1458 cancontrol adjustment of white balance based upon ROI of object within ascene. An example AWB block 1458 follows rules similar to gain and AEfor noise, and saturation. An example HDR block 1456 operates similar tothe AE block 1452 and the gain block 1450. In an HDR in multi-framescenario the HADR block 1456 is configured to minimize artifacts fromfast moving objects, for example.

FIG. 15 is an illustrative drawing showing an example camera imagingsystem 1500 that includes radar tracking and camera illumination. Theimaging system 1500 includes first, second, and third cameras 1502-1,1502-2, 1502-3, a radar unit 1504, and first and second illuminationdevices 1560-1, 1560-2. The illumination devices can be visual or IR,for example, to match camera-type. 1R also can provide night visioncapability. The first, second, and third cameras 1502-1, 1502-2, 1502-3capture respective first, second, and third camera images of an object1505. The radar unit 1504 can determine distance of the object 1505,speed of the object 1505 and a classification to associate with theobject 1505. First and second illumination devices 1560-1, 1560-2illuminate the object 1505 with a light intensity determined based uponone or more the radar-determined distance, speed, and classification. Asexplained above, balance illumination, exposure and gain are balanced toilluminate the scene while reducing blur and minimizing noise. FIG. 16is an illustrative schematic block diagram showing certain details of anexample camera imaging system 1600 that includes radar tracking andcamera illumination. The imaging system 1600 includes ta camera system1602, a radar unit 1604, an illumination system 1680, and a computersystem described below with reference to FIG. 17, including one or moreprocessor devices configured to operatively couple to one or morenon-transitory storage devices that store instructions to configure theprocessing devices to perform the block functions described as follows.An example camera system 1602 includes multiple cameras 1670-1, 1670-2,1670-3 and an intrinsic camera adjustment block 1612.

Each camera 1670-1, 1670-2, 1670-3 includes a respective lens system(not shown), a respective image sensor (not shown) and a respectiveimage processing block 1610-1, 1610-2, 1610-3. Each camera operates asrespective image frame processing pipeline that processes a respectivesequence of image data frames. The cameras 1670-1, 1670-2, 1670-3 can becalibrated according to geometric parameters as described above tocorrelate corresponding coordinate locations in their respective pixelsensor image planes. Each camera also includes an input image databuffer (not shown).

An example adjustment block 1612 adjusts intrinsic parameters of themultiple cameras based upon respective first, second, and thirdluminance information L1, L2, L3 determined by first, second, and thirdimage processing blocks 1610-1, 1610-2, 1610-3 and based upon one orboth of radar-determined speed and radar-determined classification of acamera target object. An example camera system 1402 balance illuminationwith power conservation. In an example camera system 1402, how muchillumination to use is based on the class of object. If the objectrequires a high resolution image, such as a human face, for example,sufficient illumination is provided to perceive the face; if on theother hand the object does not require a high resolution image, such asan animal, for example, sufficient illumination is provided to perceivethe type of animal while preserving my illumination power.

More specifically, for example, the adjustment block 1612 producesrespective first, second and third adjustment parameters P1, P2, P3 toadjust image capture and processing by the respective first, second andthird cameras 1670-1, 1670-2, 1670-3. The first, second, and thirdadjustment parameters P1, P2, P3 can be determined, for example, toachieve uniform image luminance within the first, second, and thirdcameras. Additionally, the first, second, and third adjustmentparameters P1, P2, P3 can be selected, for example, to reduce blurringof respective images of a camera target object captured by the first,second, and third cameras.

The illumination system 1680 includes multiple illumination devices1682-1 to 1682-n that can be positioned to illuminate a scene capturedby the multiple cameras 1670-1, 1670-2, 1670-3. The illumination devicescan be at distributed locations so that different ones of theillumination devices are positioned to provide illumination withinfields of view of different ones of the cameras. An illumination controlblock 1684 adjusts illumination power to provide to the different onesof the illumination devices based upon one or both of radar-determineddistance and radar-determined classification of a camera target object.For instance, an example illumination block 1684 can cause higherillumination by the illumination devices 1682-1 to 1682-n in response toa camera target object that is more distant than in response to a cameratracking object that is closer. Moreover, an example illumination block1684 can cause higher illumination by the illumination devices 1682-1 to1682-n in response to a camera target object that is morelight-reflective than in response to a camera tracking object that isless light-reflective. Furthermore, the example illumination block 1684can cause different illumination devices to illuminate with differentlevels of illumination.

More particularly, for example, the illumination devices can beconfigured such that, if object is too far and it is of class ofinterest, also if the ALS or Luminance channel show low illumination,then a high illumination power setting is used for illumination of ascene. The example lamination devices are configured such that if anobject is too close then a mild illumination power setting is used toavoid saturating while still sufficiently illuminating the scene so thatdetails are perceivable to a human. The example lamination devices areconfigured to implement a functional control loop betweendistance/speed, object class, current illumination or the object(through ALS or Luminance channel) to decide how high or low to changeillumination. For example, assume that radar detects a person atdistance of 20 meters moving toward a zone of interest observed by thecamera imaging system 1600. Assume further that the camera frames of thecamera imaging system 1600 detects ROIs as very dark. In response todetermining that the ROIs are dark, the camera system 1600 causesilluminator power to be set at a higher value and also adjusts 3Aparameters. The camera system 1600 then checks ROI to determine whetherluminance is improved. If illumination is still below an acceptablethreshold illumination, then illumination can be increased further. Ifillumination is above an acceptable threshold, then illumination can bedecreased.

The radar unit 1604 includes blocks 1603 and 1626-1638, which will beunderstood from the description above with reference to FIG. 13. Foreconomy of description, the description of these blocks is not repeated.

Computing Machine

FIG. 17 is an illustrative block diagram of a computing machine 1700 inaccordance with some embodiments. In some embodiments, the computingmachine 1700 may store the components shown in the circuit block diagramof FIG. 17. For example, circuitry that resides in the processor 1702and may be referred to as “processing circuitry.” Processing circuitrymay include processing hardware, for example, one or more centralprocessing units (CPUs), one or more graphics processing units (GPUs),and the like. In alternative embodiments, the computing machine 1700 mayoperate as a standalone device or may be connected (e.g., networked) toother computers. In a networked deployment, the computing machine 1700may operate in the capacity of a server, a client, or both inserver-client network environments. In an example, the computing machine1700 may act as a peer machine in peer-to-peer (P2P) (or otherdistributed) network environment. In this document, the phrases P2P,device-to-device (D2D) and sidelink may be used interchangeably. Thecomputing machine 1700 may be a specialized computer, a personalcomputer (PC), a tablet PC, a personal digital assistant (PDA), a mobiletelephone, a smart phone, a web appliance, a network router, switch orbridge, or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules and componentsare tangible entities (e.g., hardware) capable of performing specifiedoperations and may be configured or arranged in a certain manner. In anexample, circuits may be arranged (e.g., internally or with respect toexternal entities such as other circuits) in a specified manner as amodule. In an example, the whole or part of one or more computersystems/apparatus (e.g., a standalone, client or server computer system)or one or more hardware processors may be configured by firmware orsoftware (e.g., instructions, an application portion, or an application)as a module that operates to perform specified operations. In anexample, the software may reside on a machine readable medium. In anexample, the software, when executed by the underlying hardware of themodule, causes the hardware to perform the specified operations.

Accordingly, the term “module” (and “component”) is understood toencompass a tangible entity, be that an entity that is physicallyconstructed, specifically configured (e.g., hardwired), or temporarily(e.g., transitorily) configured (e.g., programmed) to operate in aspecified manner or to perform part or all of any operation describedherein. Considering examples in which modules are temporarilyconfigured, each of the modules need not be instantiated at any onemoment in time. For example, where the modules comprise ageneral-purpose hardware processor configured using software, thegeneral-purpose hardware processor may be configured as respectivedifferent modules at different times. Software may accordingly configurea hardware processor, for example, to constitute a particular module atone instance of time and to constitute a different module at a differentinstance of time.

The computing machine 1700 may include a hardware processor 1702 (e.g.,a central processing unit (CPU), a GPU, a hardware processor core, orany combination thereof), a main memory 1704 and a static memory 1706,some or all of which may communicate with each other via an interlink(e.g., bus) 1708. Although not shown, the main memory 1704 may containany or all of removable storage and non-removable storage, volatilememory, or non-volatile memory. The computing machine 1700 may furtherinclude a video display unit 1710 (or other display unit), analphanumeric input device 1717 (e.g., a keyboard), and a user interface(UI) navigation device 1714 (e.g., a mouse). In an example, the displayunit 1710, input device 1717 and UI navigation device 1714 may be atouch screen display. The computing machine 1700 may additionallyinclude a storage device (e.g., drive unit) 1716, a signal generationdevice 1718 (e.g., a speaker), a network interface device 1720, and oneor more sensors 1721, such as a global positioning system (GPS) sensor,compass, accelerometer, or other sensor. The computing machine 1700 mayinclude an output controller 1728, such as a serial (e.g., universalserial bus (USB), parallel, or other wired or wireless (e.g., infrared(IR), near field communication (NFC), etc.) connection to communicate orcontrol one or more peripheral devices (e.g., a printer, card reader,etc.).

The drive unit 1716 (e.g., a storage device) may include a machinereadable medium 1722 on which is stored one or more sets of datastructures or instructions 1724 (e.g., software) embodying or utilizedby any one or more of the techniques or functions described herein. Theinstructions 1724 may also reside, completely or at least partially,within the main memory 1704, within static memory 1706, or within thehardware processor 1702 during execution thereof by the computingmachine 1700. In an example, one or any combination of the hardwareprocessor 1702, the main memory 1704, the static memory 1706, or thestorage device 1716 may constitute machine readable media.

While the machine readable medium 1722 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 1724.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe computing machine 1700 and that cause the computing machine 1700 toperform any one or more of the techniques of the present disclosure, orthat is capable of storing, encoding or carrying data structures used byor associated with such instructions. Non-limiting machine readablemedium examples may include solid-state memories, and optical andmagnetic media. Specific examples of machine readable media may include:non-volatile memory, such as semiconductor memory devices (e.g.,Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; Random Access Memory (RAM); and CD-ROM andDVD-ROM disks. In some examples, machine readable media may includenon-transitory machine-readable media. In some examples, machinereadable media may include machine readable media that is not atransitory propagating signal.

The instructions 1724 may further be transmitted or received over acommunications network 1726 using a transmission medium via the networkinterface device 1720 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards, a LongTerm Evolution (LTE) family of standards, a Universal MobileTelecommunications System (UMTS) family of standards, peer-to-peer (P2P)networks, among others. In an example, the network interface device 1720may include one or more physical jacks (e.g., Ethernet, coaxial, orphone jacks) or one or more antennas to connect to the communicationsnetwork 1726.

Machine Learning

FIGS. 18-20 are illustrative drawings describing machine learningtechniques and systems that can be used to train blocks 706, 714, 812,822, 912, 922, 1122, 1332, and 1632, for example, in accordance withsome embodiments. FIG. 18 illustrates the training and use of amachine-learning program, according to some example embodiments. In someexample embodiments, machine-learning programs (MLPs), also referred toas machine-learning algorithms or tools, are utilized to performoperations associated with machine learning tasks, such as imagerecognition or machine translation.

Machine learning is a field of study that gives computers the ability tolearn without being explicitly programmed. Machine learning explores thestudy and construction of algorithms, also referred to herein as tools,which may learn from existing data and make predictions about new data.Such machine-learning tools operate by building a model from exampletraining data 1818 in order to make data-driven predictions or decisionsexpressed as outputs or assessments 1820. Although example embodimentsare presented with respect to a few machine-learning tools, theprinciples presented herein may be applied to other machine-learningtools.

In some example embodiments, different machine-learning tools may beused. For example, Logistic Regression (LR), Naive-Bayes, Random Forest(RF), neural networks (NN), matrix factorization, and Support VectorMachines (SVM) tools may be used for classifying or scoring jobpostings.

Two common types of problems in machine learning are classificationproblems and regression problems. Classification problems, also referredto as categorization problems, aim at classifying items into one ofseveral category values (for example, is this object an apple or anorange). Regression algorithms aim at quantifying some items (forexample, by providing a value that is a real number). Themachine-learning algorithms utilize the training data 1818 to findcorrelations among identified features 1802 that affect the outcome.

The machine-learning algorithms utilize features 1802 for analyzing thedata to generate assessments 1820. A feature 1802 is an individualmeasurable property of a phenomenon being observed. The concept of afeature is related to that of an explanatory variable used instatistical techniques such as linear regression. Choosing informative,discriminating, and independent features is important for effectiveoperation of the MLP in pattern recognition, classification, andregression. Features may be of different types, such as numericfeatures, strings, and graphs.

In one example embodiment, the features 1802 may be of different typesand may include one or more of words of the message 1803, messageconcepts 1804, communication history 1805, past user behavior 1806,subject of the message 1807, other message attributes 1808, sender 1809,and user data 1880.

The machine-learning algorithms utilize the training data 1818 to findcorrelations among the identified features 1802 that affect the outcomeor assessment 1820. In some example embodiments, the training data 1812includes labeled data, which is known data for one or more identifiedfeatures 1802 and one or more outcomes, such as detecting communicationpatterns, detecting the meaning of the message, generating a summary ofthe message, detecting action items in the message, detecting urgency inthe message, detecting a relationship of the user to the sender,calculating score attributes, calculating message scores, etc.

With the training data 1812 and the identified features 1802, themachine-learning tool is trained at operation 1814. The machine-learningtool appraises the value of the features 1802 as they correlate to thetraining data 1812. The result of the training is the trainedmachine-learning program 1816.

When the machine-learning program 1816 is used to perform an assessment,new data 1818 is provided as an input to the trained machine-learningprogram 1816, and the machine-learning program 1816 generates theassessment 1820 as output. For example, when a message is checked for anaction item, the machine-learning program utilizes the message contentand message metadata to determine if there is a request for an action inthe message.

Machine learning techniques train models to accurately make predictionson data fed into the models (e.g., what was said by a user in a givenutterance; whether a noun is a person, place, or thing; what the weatherwill be like tomorrow). During a learning phase, the models aredeveloped against a training dataset of inputs to optimize the models tocorrectly predict the output for a given input. Generally, the learningphase may be supervised, semi-supervised, or unsupervised, indicating adecreasing level to which the “correct” outputs are provided incorrespondence to the training inputs. In a supervised learning phase,all of the outputs are provided to the model and the model is directedto develop a general rule or algorithm that maps the input to theoutput. In contrast, in an unsupervised learning phase, the desiredoutput is not provided for the inputs so that the model may develop itsown rules to discover relationships within the training dataset. In asemi-supervised learning phase, an incompletely labeled training set isprovided, with some of the outputs known and some unknown for thetraining dataset.

Models may be run against a training dataset for several epochs (e.g.,iterations), in which the training dataset is repeatedly fed into themodel to refine its results. For example, in a supervised learningphase, a model is developed to predict the output for a given set ofinputs, and is evaluated over several epochs to more reliably providethe output that is specified as corresponding to the given input for thegreatest number of inputs for the training dataset. In another example,for an unsupervised learning phase, a model is developed to cluster thedataset into n groups, and is evaluated over several epochs as to howconsistently it places a given input into a given group and how reliablyit produces the n desired clusters across each epoch.

Once an epoch is run, the models are evaluated and the values of theirvariables are adjusted to attempt to better refine the model in aniterative fashion. In various aspects, the evaluations are biasedagainst false negatives, biased against false positives, or evenlybiased with respect to the overall accuracy of the model. The values maybe adjusted in several ways depending on the machine learning techniqueused. For example, in a genetic or evolutionary algorithm, the valuesfor the models that are most successful in predicting the desiredoutputs are used to develop values for models to use during thesubsequent epoch, which may include random variation/mutation to provideadditional data points. One of ordinary skill in the art will befamiliar with several other machine learning algorithms that may beapplied with the present disclosure, including linear regression, randomforests, decision tree learning, neural networks, deep neural networks,etc.

Each model develops a rule or algorithm over several epochs by varyingthe values of one or more variables affecting the inputs to more closelymap to a desired result, but as the training dataset may be varied, andis preferably very large, perfect accuracy and precision may not beachievable. A number of epochs that make up a learning phase, therefore,may be set as a given number of trials or a fixed time/computing budget,or may be terminated before that number/budget is reached when theaccuracy of a given model is high enough or low enough or an accuracyplateau has been reached. For example, if the training phase is designedto run n epochs and produce a model with at least 95% accuracy, and sucha model is produced before the n^(th) epoch, the learning phase may endearly and use the produced model, satisfying the end-goal accuracythreshold. Similarly, if a given model is inaccurate enough to satisfy arandom chance threshold (e.g., the model is only 55% accurate indetermining true/false outputs for given inputs), the learning phase forthat model may be terminated early, although other models in thelearning phase may continue training. Similarly, when a given modelcontinues to provide similar accuracy or vacillate in its results acrossmultiple epochs—having reached a performance plateau—the learning phasefor the given model may terminate before the epoch number/computingbudget is reached.

Once the learning phase is complete, the models are finalized. In someexample embodiments, models that are finalized are evaluated againsttesting criteria. In a first example, a testing dataset that includesknown outputs for its inputs is fed into the finalized models todetermine an accuracy of the model in handling data that it has not beentrained on. In a second example, a false positive rate or false negativerate may be used to evaluate the models after finalization. In a thirdexample, a delineation between data clusterings is used to select amodel that produces the clearest bounds for its clusters of data.

FIG. 19 illustrates an example neural network 1904, in accordance withsome embodiments. As shown, the neural network 1904 receives, as input,source domain data 1902. The input is passed through a plurality oflayers 1906 to arrive at an output. Each layer 1906 includes multipleneurons 1908. The neurons 1908 receive input from neurons of a previouslayer 1906 and apply weights to the values received from those neurons1908 in order to generate a neuron output. The neuron outputs from thefinal layer 1906 are combined to generate the output of the neuralnetwork 1904.

As illustrated at the bottom of FIG. 19, the input is a vector x. Theinput is passed through multiple layers 1906, where weights W₁, W₂, . .. , W_(i) are applied to the input to each layer to arrive at f¹(x),f²(x), . . . , f^(i-1)(x), until finally the output f(x) is computed.

In some example embodiments, the neural network 1904 (e.g., deeplearning, deep convolutional, or recurrent neural network) comprises aseries of neurons 1908, such as Long Short Term Memory (LSTM) nodes,arranged into a network. A neuron 1908 is an architectural element usedin data processing and artificial intelligence, particularly machinelearning, which includes memory that may determine when to “remember”and when to “forget” values held in that memory based on the weights ofinputs provided to the given neuron 1908. Each of the neurons 1908 usedherein is configured to accept a predefined number of inputs from otherneurons 1908 in the neural network 1904 to provide relational andsub-relational outputs for the content of the frames being analyzed.Individual neurons 1908 may be chained together and/or organized intotree structures in various configurations of neural networks to provideinteractions and relationship learning modeling for how each of theframes in an utterance are related to one another.

For example, an LSTM serving as a neuron includes several gates tohandle input vectors (e.g., phonemes from an utterance), a memory cell,and an output vector (e.g., contextual representation). The input gateand output gate control the information flowing into and out of thememory cell, respectively, whereas forget gates optionally removeinformation from the memory cell based on the inputs from linked cellsearlier in the neural network. Weights and bias vectors for the variousgates are adjusted over the course of a training phase, and once thetraining phase is complete, those weights and biases are finalized fornormal operation. One of skill in the art will appreciate that neuronsand neural networks may be constructed programmatically (e.g., viasoftware instructions) or via specialized hardware linking each neuronto form the neural network.

Neural networks utilize features for analyzing the data to generateassessments (e.g., recognize units of speech). A feature is anindividual measurable property of a phenomenon being observed. Theconcept of feature is related to that of an explanatory variable used instatistical techniques such as linear regression. Further, deep featuresrepresent the output of nodes in hidden layers of the deep neuralnetwork.

A neural network, sometimes referred to as an artificial neural network,is a computing system/apparatus based on consideration of biologicalneural networks of animal brains. Such systems/apparatus progressivelyimprove performance, which is referred to as learning, to perform tasks,typically without task-specific programming. For example, in imagerecognition, a neural network may be taught to identify images thatcontain an object by analyzing example images that have been tagged witha name for the object and, having learnt the object and name, may usethe analytic results to identify the object in untagged images. A neuralnetwork is based on a collection of connected units called neurons,where each connection, called a synapse, between neurons can transmit aunidirectional signal with an activating strength that varies with thestrength of the connection. The receiving neuron can activate andpropagate a signal to downstream neurons connected to it, typicallybased on whether the combined incoming signals, which are frompotentially many transmitting neurons, are of sufficient strength, wherestrength is a parameter.

A deep neural network (DNN) is a stacked neural network, which iscomposed of multiple layers. The layers are composed of nodes, which arelocations where computation occurs, loosely patterned on a neuron in thehuman brain, which fires when it encounters sufficient stimuli. A nodecombines input from the data with a set of coefficients, or weights,that either amplify or dampen that input, which assigns significance toinputs for the task the algorithm is trying to learn. These input-weightproducts are summed, and the sum is passed through what is called anode's activation function, to determine whether and to what extent thatsignal progresses further through the network to affect the ultimateoutcome. A DNN uses a cascade of many layers of non-linear processingunits for feature extraction and transformation. Each successive layeruses the output from the previous layer as input. Higher-level featuresare derived from lower-level features to form a hierarchicalrepresentation. The layers following the input layer may be convolutionlayers that produce feature maps that are filtering results of theinputs and are used by the next convolution layer.

In training of a DNN architecture, a regression, which is structured asa set of statistical processes for estimating the relationships amongvariables, can include a minimization of a cost function. The costfunction may be implemented as a function to return a numberrepresenting how well the neural network performed in mapping trainingexamples to correct output. In training, if the cost function value isnot within a pre-determined range, based on the known training images,backpropagation is used, where backpropagation is a common method oftraining artificial neural networks that are used with an optimizationmethod such as a stochastic gradient descent (SGD) method.

Use of backpropagation can include propagation and weight update. Whenan input is presented to the neural network, it is propagated forwardthrough the neural network, layer by layer, until it reaches the outputlayer. The output of the neural network is then compared to the desiredoutput, using the cost function, and an error value is calculated foreach of the nodes in the output layer. The error values are propagatedbackwards, starting from the output, until each node has an associatederror value which roughly represents its contribution to the originaloutput. Backpropagation can use these error values to calculate thegradient of the cost function with respect to the weights in the neuralnetwork. The calculated gradient is fed to the selected optimizationmethod to update the weights to attempt to minimize the cost function.

FIG. 20 illustrates the training of an image recognition machinelearning program, in accordance with some embodiments. The machinelearning program may be implemented at one or more computing machines.As shown, training set 2002 includes multiple classes 2004. Each class2004 includes multiple images 2006 associated with the class. Each class2004 may correspond to a type of object in the image 2006 (e.g., a digit0-9, a man or a woman, a cat or a dog, etc.). In one example, themachine learning program is trained to recognize images of thepresidents of the United States, and each class corresponds to eachpresident (e.g., one class corresponds to Donald Trump, one classcorresponds to Barack Obama, one class corresponds to George W. Bush,etc.). At block 2008 the machine learning program is trained, forexample, using a deep neural network. The trained classifier 310,generated by the training of block 2008, recognizes an image 312, and atblock 314 the image is recognized. For example, if the image 312 is aphotograph of Bill Clinton, the classifier recognizes the image ascorresponding to Bill Clinton at block 314.

FIG. 20 illustrates the training of a classifier, according to someexample embodiments. A machine learning algorithm is designed forrecognizing faces, and a training set 2002 includes data that maps asample to a class 2004 (e.g., a class includes all the images ofpurses). The classes may also be referred to as labels or annotations.Although embodiments presented herein are presented with reference toobject recognition, the same principles may be applied to trainmachine-learning programs used for recognizing any type of items.

The training set 2002 includes a plurality of images 2006 for each class2004 (e.g., image 2006), and each image is associated with one of thecategories to be recognized (e.g., a class). The machine learningprogram is trained at block 2008 with the training data to generate aclassifier at block 2010 operable to recognize images. In some exampleembodiments, the machine learning program is a DNN.

When an input image 2012 is to be recognized, the classifier 2010analyzes the input image 2012 to identify the class corresponding to theinput image 2012. This class is labeled in the recognized image at block2014.

FIG. 21 illustrates the feature-extraction process and classifiertraining, according to some example embodiments. Training the classifiermay be divided into feature extraction layers 2102 and classifier layer2114. Each image is analyzed in sequence by a plurality of layers2106-2113 in the feature-extraction layers 2102.

With the development of deep convolutional neural networks, the focus inface recognition has been to learn a good face feature space, in whichfaces of the same person are close to each other, and faces of differentpersons are far away from each other. For example, the verification taskwith the LFW (Labeled Faces in the Wild) dataset has often been used forface verification.

Many face identification tasks (e.g., MegaFace and LFW) are based on asimilarity comparison between the images in the gallery set and thequery set, which is essentially a K-nearest-neighborhood (KNN) method toestimate the person's identity. In the ideal case, there is a good facefeature extractor (inter-class distance is always larger than theintra-class distance), and the KNN method is adequate to estimate theperson's identity.

Feature extraction is a process to reduce the amount of resourcesrequired to describe a large set of data. When performing analysis ofcomplex data, one of the major problems stems from the number ofvariables involved. Analysis with a large number of variables generallyrequires a large amount of memory and computational power, and it maycause a classification algorithm to overfit to training samples andgeneralize poorly to new samples. Feature extraction is a general termdescribing methods of constructing combinations of variables to getaround these large data-set problems while still describing the datawith sufficient accuracy for the desired purpose.

In some example embodiments, feature extraction starts from an initialset of measured data and builds derived values (features) intended to beinformative and non-redundant, facilitating the subsequent learning andgeneralization steps. Further, feature extraction is related todimensionality reduction, such as by reducing large vectors (sometimeswith very sparse data) to smaller vectors capturing the same, orsimilar, amount of information.

Determining a subset of the initial features is called featureselection. The selected features are expected to contain the relevantinformation from the input data, so that the desired task can beperformed by using this reduced representation instead of the completeinitial data. DNN utilizes a stack of layers, where each layer performsa function. For example, the layer could be a convolution, a non-lineartransform, the calculation of an average, etc. Eventually this DNNproduces outputs by classifier 2114. In FIG. 21, the data travels fromleft to right as the features are extracted. The goal of training theneural network is to find the parameters of all the layers that makethem adequate for the desired task.

Various Geometric Parameter Calibration Examples

Example 1 can include an imaging system comprising: a memory storinginstructions which, when executed by the processing circuitry, cause theprocessing circuitry to perform operations comprising: in a calibrationmode, receiving, using a first camera, image information projected at animage plane of the first camera from a 3D world; determining based uponthe image information, multiple image regions of interest (ROIs),wherein individual ones of the multiple image ROIs correspond to one ormore three-dimensional (3D) world object images projected at the imageplane; receiving, using a radar unit, radar information indicating oneor more 3D world objects; determining based upon the radar information,multiple respective radar ROIs, wherein individual ones of the multipleradar ROIs correspond to one or more 3D world objects indicated by theradar information; determining based upon the radar information,respective 3D world distances corresponding to respective radar ROIs;determining multiple co-registered ROI pairs by co-registeringindividual image ROIs with individual radar ROIs corresponding to common3D world objects; adjusting one or more first geometric parametersassociated with the first camera, based upon the co-registered ROI pairsto produce adjusted first geometric parameters.

Example 2 can include the subject matter of Example 1 further including:adjusting one or more second geometric parameters associated with asecond camera based upon the adjusted first geometric parameters toproduce adjusted second geometric parameters.

Example 3 can include the subject matter of Example 1 wherein adjustingthe one or more first geometric parameters includes for respective onesof the multiple respective co-registered ROI pairs, adjusting based uponat least one respective two-dimensional coordinate location at the imageplane of the first camera where an image is projected of a 3D worldobject corresponding to a respective image ROI of the respectiveco-registered ROI pair and upon a respective 3D world distancecorresponding to a respective radar unit ROI of the respectiveco-registered ROI pair.

Example 4 can include the subject matter of Example 1 the operationsfurther comprising: determining a scaling factor based at least in partupon one or more of the respective 3D world distances corresponding torespective radar ROIs; wherein adjusting includes adjusting based atleast in part upon the determined scaling factor.

Example 5 can include the subject matter of Example 1 the operationsfurther including: classifying respective image ROIs, using a firsttrained machine learning engine; classifying respective radar unit ROIs,using a second trained machine learning engine; wherein determining themultiple co-registered ROI pairs includes matching respective image ROIsand respective radar ROIs of respective co-registered pair, based uponrespective image ROI classifications and respective radar ROIclassifications.

Example 6 can include the subject matter of Example 5 whereinclassifying respective image ROIs, using a first trained machinelearning engine, includes classifying based upon semantic segmentation.

Example 7 can include the subject matter of Example 6 the operationsfurther including: conditioning performance of the act of adjusting upona change in a 3D world distance corresponding to a respective radar ROIof at least one co-registered pair.

Example 8 can include the subject matter of Example 1 the operationsfurther including: conditioning performance of the act of adjusting upona threshold count of co-registered ROI pairs.

Example 9 can include the subject matter of Example 1 the operationsfurther including: classifying respective image ROIs and producingcorresponding image classification confidence scores, using a firsttrained machine learning engine; classifying respective radar unit ROIsand producing corresponding radar classification confidence scores,using a second trained machine learning engine; wherein determining themultiple co-registered ROI pairs includes matching respective image ROIsand respective radar ROIs of respective co-registered pair, based uponrespective image ROI classifications and respective radar ROIclassifications; further including: conditioning use of respectiveco-registered ROI pairs in the act adjusting upon respective imageclassification confidence levels and respective radar classificationlevels of respective image ROIs and respective radar ROIs of respectiveco-registered ROI pairs.

Example 10 can include the subject matter of Example 1 the operationsfurther including: for respective ones of the multiple respectiveco-registered ROI pairs, determining, using the first camera, arespective first distance from a respective common 3D world objectcorresponding to the respective image ROI and the respective radar ROIof the respective ROI pair; determining, using the radar unit, arespective second distance from the respective common 3D world objectcorresponding to the respective image ROI and the respective radar ROIof the respective ROT pair; and conditioning use of respectiveco-registered ROI pairs in the act adjusting upon respective a thresholdloss difference between respective image ROIs and radar ROIs of therespective ROI pairs.

Example 11 can include the subject matter of Example 10 wherein the lossthreshold difference includes a distance threshold loss.

Example 12 can include the subject matter of Example 10 the operationsfurther including: tracking respective trajectories of respective imageROIs; tracking respective trajectories of respective radar ROIs; whereinthe loss threshold difference includes a tracking trajectory thresholdloss.

Example 13 can include the subject matter of Example 1 the operationsfurther including: periodically triggering the act of adjusting.

Example 14 can include the subject matter of Example 1 the operationsfurther including: triggering the act of adjusting in response tooccurrence of an external event.

Example 15 can include the subject matter of Example 1 wherein the oneor more first geometric parameters associated with the first camerainclude first, second and third rotation parameters and include a heightparameter.

Example 16 can include the subject matter of Example 1 wherein the oneor more first geometric parameters associated with the first camerainclude first respective first, second and third rotation parameters andfirst respective first, second and third translation parameters; andwherein the one or more second geometric parameters associated with thesecond camera include second respective first, second and third rotationparameters and second respective first, second and third translationparameters.

Example 17 can include the subject matter of Example 1, furtherincluding: the processing circuitry; the first camera; and the radarunit, wherein the radar unit has a frame of reference geometricallyregistered with a frame of reference of the first camera.

Example 18 can include the subject matter of Example 2 furtherincluding: the processing circuitry; the first camera; the secondcamera; and the radar unit; wherein the radar unit has a frame ofreference geometrically registered with a frame of reference of thefirst camera.

Example 19 can include the subject matter of Example 1 wherein adjustingthe one or more first geometric parameters includes for respective onesof the multiple respective co-registered ROI pairs, determining arespective projection between a respective two-dimensional coordinatelocation, at the image plane where an image is projected of a 3D worldobject corresponding to a respective image ROI of the respectiveco-registered ROI pair, and a respective 3D world coordinate location ofa 3D world object corresponding to a respective radar unit ROI of therespective co-registered ROI pair, based upon a first matrix functionincluding the first geometric parameters; and estimating the adjustedfirst geometric parameters based upon a parameter fitting process usingthe respective projections.

Example 20 can include the subject matter of Example 2 wherein adjustingthe one or more first geometric parameters includes for respective onesof the multiple respective co-registered ROI pairs, determining arespective first projection between a respective two-dimensionalcoordinate location, at the image plane where an image is projected of a3D world object corresponding to a respective image ROI of therespective co-registered ROI pair, and a respective 3D world coordinatelocation of a 3D world object corresponding to a respective radar unitROI of the respective co-registered ROI pair, based upon a first matrixfunction including the first geometric parameters; and estimating theadjusted first geometric parameters based upon a first parameter fittingprocess using the respective first projections; wherein adjusting theone or more second geometric parameters includes, determining respectivesecond projections between respective two-dimensional coordinatelocations, at the image plane of the first camera and respectivetwo-dimensional coordinate locations at an image plane of the secondcamera, based upon the first matrix function and based upon a secondmatrix function including the second geometric parameters; andestimating the adjusted second geometric parameters based upon a secondparameter fitting process using the respective second projections.

Example 21 can include the subject matter of Example 1 the operationsfurther comprising: in a functional mode, receiving, using the firstcamera, image information including a projection including an image of a3D world object at the image plane of the first camera; and determininga distance from the 3D world object based upon the projection at theimage plane and the adjusted first geometric parameters.

Example 22 can include the subject matter of Example 1 the operationsfurther comprising: in a functional mode, receiving, using the firstcamera, image information including a first projection including animage of a 3D world object at the image plane of the first camera;receiving, using the second camera, image information including a secondprojection including an image of the 3D world object at an image planeof the second camera; and determining a distance from the 3D worldobject based upon the first projection at the image plane of the firstcamera, the second projection at the image plane of the second cameraand the adjusted first and second geometric parameters.

Various Photometric Parameter Calibration Examples

Example 23 can include an imaging system comprising: a memory storinginstructions which, when executed by the processing circuitry, cause theprocessing circuitry to perform operations comprising: receiving firstimage information including an image of an object, using a first cameracalibrated using a first group of intrinsic parameters values;determining, using the first camera, luminance information associatedwith the received first image information; receiving, using a radarunit, radar information indicating an object; determining based upon theradar information, a speed to associate with the object; and adjustingone or more photometric parameters of the first group based upon thedetermined luminance information and the associated speed of the object.

Example 24 can include the subject matter of Example 23 the operationsfurther including: classifying the object, using a trained machinelearning engine; wherein adjusting further includes adjusting based atleast in part upon classification of the object.

Example 25 can include the subject matter of Example 23 whereinclassifying the object further includes classifying, using the radarunit configured with a trained machine learning engine.

Example 26 can include the subject matter of Example 23 wherein thefirst group of photometric parameters values includes a gain parametervalue; and wherein adjusting one or more values of the first group ofphotometric parameters values in includes adjusting the gain parametervalue based upon the determined luminance information and the trackedspeed.

Example 27 can include the subject matter of Example 23 wherein thefirst group of photometric parameters values includes an exposureparameter value; and wherein adjusting one or more values of the firstgroup of photometric parameters values in includes adjusting theexposure parameter value based upon the determined luminance informationand the tracked speed.

Example 28 can include the subject matter of Example 23 wherein firstgroup of photometric parameters values includes an automatic whitebalance (AWB) parameter value; and the operations further including:modifying white balance within the received information based upon theAWB parameter value; wherein adjusting one or more values of the firstgroup of photometric parameters values in includes adjusting the AWBparameter value based upon the determined luminance information and thetracked speed.

Example 29 can include the subject matter of Example 23 wherein thefirst group of photometric parameters values includes a high dynamicrange (HDR) parameter value; and the operations further including:modifying the received first image information based upon the HDRparameter value; wherein adjusting one or more values of the first groupof photometric parameters values in includes adjusting the HDR parametervalue based upon the determined luminance information and the trackedspeed.

Example 30 can include the subject matter of Example 23 the operationsfurther including: determining an image region of interest (ROT) withinthe first image information that corresponds to an image object;receiving, using the radar unit, radar information; determining a radarROI within the radar information that corresponds to a radar object;determining a co-registered ROI pair by co-registering the image ROIwith the radar ROT; and determining that the image ROI of theco-registered ROI pair corresponds to the object.

Example 31 can include the subject matter of Example 30 the operationsfurther including: associating the speed of the object with the receivedfirst image information based upon the determination that image ROI ofthe co-registered ROI pair corresponds to the object.

Example 32 can include the subject matter of Example 30 the operationsfurther including: classifying the image ROI, using a first trainedmachine learning engine; classifying the radar unit ROI, using a secondtrained machine learning engine; wherein determining the co-registeredROI pair includes matching the image ROIs and the radar ROI, based upona respective image ROI classification and a respective radar ROIclassification.

Example 33 can include the subject matter of Example 32 whereinadjusting further includes adjusting based at least in part uponclassification of the object.

Example 34 can include the subject matter of Example 23 the operationsfurther including: receiving second image information including an imageof the object, using a second camera calibrated using a second group ofphotometric parameters values; determining, using the second camera,second luminance information associated with the second imageinformation received using the second camera; wherein adjusting furtherincludes adjusting one or more values of the second group of photometricparameters values based upon the determined second luminance informationand the tracked speed.

Example 36 can include the subject matter of Example 35 the operationsfurther including: receiving second image information including an imageof the object, using a second camera calibrated using a second group ofphotometric parameters values; determining, using the second camera,second luminance information associated with the second imageinformation received using the second camera; tracking, using a radarunit, distance between the object and the radar unit; wherein adjustingfurther includes adjusting one or more values of the first group ofphotometric parameters values based upon the first luminanceinformation, the second luminance information, and the tracked distance,and further includes adjusting one or more values of the second group ofphotometric parameters values based upon the first luminanceinformation, the second luminance information, and the tracked distance.

Example 36 can include the subject matter of Example 35 the operationsfurther including: determining illumination intensity of multipleillumination devices, located at different locations to illuminate theobject, based upon the tracked distance.

Example 37 can include the subject matter of Example 36 the operationsfurther including: classifying the object, using the radar unit; whereindetermining illumination intensity further includes determining, basedat least in part upon classification of the object.

Example 38 can include the subject matter of Example 37 the operationsfurther including: wherein classifying the object includes classifying,using the radar unit configured with a trained machine learning engine.

Example 39 can include the subject matter of Example 35 the operationsfurther including: determining a first image region of interest (ROI)within the first image information that corresponds to a first imageobject; determining a second image ROI within the second imageinformation that corresponds to a second image object; receiving, usingthe radar unit, radar information; determining a radar ROI within theradar information that corresponds to a radar object; determining afirst co-registered ROI pair by co-registering the first image ROI withthe radar ROI; determining a second co-registered ROI pair byco-registering the second image ROI with the radar ROI; and determiningthat the first image ROI of the second co-registered ROI paircorresponds to the object determining that the second image ROI of thesecond co-registered ROI pair corresponds to the object.

Example 40 can include the subject matter of Example 39 the operationsfurther including: associating the speed of the object with the receivedfirst image information based upon the determination that the firstimage ROI of the first co-registered ROI pair corresponds to the object;and associating the speed of the object with the received second imageinformation based upon the determination that the second image ROI ofthe second co-registered ROI pair corresponds to the object.

Example 41 can include the subject matter of Example 1 furtherincluding: the processing circuitry; the first camera; and the radarunit.

Example 42 can include the subject matter of Example 35 furtherincluding: the processing circuitry; the first camera; the secondcamera; and the radar unit.

Example 43 can include a memory storing instructions which, whenexecuted by the processing circuitry, cause the processing circuitry toperform operations comprising: receiving first image informationincluding an image of an object, using a first camera calibrated using afirst group of photometric parameters values; determining, using thefirst camera, respective luminance information associated with thereceived first image information; receiving, using a radar unit, radarinformation; classifying the object, based upon the radar information,using a trained machine learning engine; adjusting one or more values ofthe first group of photometric parameters values based upon thedetermined luminance information and the classification.

Example 44 can include the operations of Example 43, further including:receiving second image information including an image of the object,using a second camera calibrated using a second group of photometricparameters values; determining, using the second camera, secondluminance information associated with the second image informationreceived using the second camera; classifying the object, based upon theradar information, using a trained machine learning engine; tracking,using a radar unit, distance between the object and the radar unit;wherein adjusting further includes adjusting one or more values of thefirst group of photometric parameters values based upon the firstluminance information, the second luminance information, and theclassification; and further includes adjusting one or more values of thesecond group of photometric parameters values based upon the firstluminance information, the second luminance information, and theclassification.

Example 45 can include an the operations further including: determiningillumination intensity of multiple illumination devices, located atdifferent locations to illuminate the object, based upon the trackeddistance.

Example 46 can include a memory storing instructions which, whenexecuted by the processing circuitry, cause the processing circuitry toperform operations comprising acts of: receiving, using a first cameracalibrated using a first group of photometric parameters that includeone or more adjustable parameter values, a first sequence of respectivefirst image frames including an image of an object; determining, usingthe first camera, a sequence of respective first luminance informationsignals corresponding to respective first image frames of the firstsequence of respective image frames; tracking, using a radar unit, speedof the object; determining in response to each of one or more respectivereceived first image frames in the sequence of respective first imageframes, one or more of adjusted parameters values, based upon respectivecorresponding luminance information signals and the tracked speed of theobject; and adjusting one or more of the adjustable parameters valueswithin the first group of photometric parameters, in response to one ormore acts of determining one or more adjusted parameters values.

Geometric Parameter Calibration Method Example

Example 47 can include a calibration method comprising: receiving, usinga first camera, image information projected at an image plane of thefirst camera from a 3D world; determining based upon the imageinformation, multiple image regions of interest (ROIs), whereinindividual ones of the multiple image ROIs correspond to one or morethree-dimensional (3D) world object images projected at the image plane;receiving, using a radar unit, radar information indicating one or more3D world objects; determining based upon the radar information, multiplerespective radar ROIs, wherein individual ones of the multiple radarROIs correspond to one or more 3D world objects indicated by the radarinformation; determining based upon the radar information, respective 3Dworld distances corresponding to respective radar ROIs; determiningmultiple co-registered ROI pairs by co-registering individual image ROIswith individual radar ROIs corresponding to common 3D world objects;adjusting one or more first geometric parameters associated with thefirst camera, based upon the co-registered ROI pairs to produce adjustedfirst geometric parameters.

Photometric Parameter Calibration Method Example

Example 48 can include a calibration method comprising: receiving firstimage information including an image of an object, using a first cameracalibrated using a first group of photometric parameters; determining,using the first camera, respective luminance information associated withthe received first image information; tracking, using a radar unit,speed of the object; and adjusting one or more photometric parameters ofthe group based upon the determined luminance information and thetracked speed of the object.

These and other features and advantages of the embodiments will beunderstood from the following claims.

1. An imaging system comprising: a memory storing instructions which,when executed by processing circuitry, cause the processing circuitry toperform operations comprising: receiving first image informationincluding an image of an object, using a first camera calibrated using afirst group of photometric parameters; determining, using the firstcamera, first luminance information associated with the received firstimage information; receiving, using a radar unit, radar informationindicating the object; determining based upon the radar information, aspeed to associate with the object; and adjusting one or more values ofthe first group of photometric parameters based upon the first luminanceinformation and the associated speed of the object.
 2. The imagingsystem of claim 1, wherein: the operations further comprise classifyingthe object, using a trained machine learning engine, and adjusting theone or more values of the first group of photometric parameters is basedat least in part upon classification of the object.
 3. The imagingsystem of claim 1, wherein: the first group of photometric parametersincludes a gain parameter; and adjusting the one or more values of thefirst group of photometric parameters comprises adjusting a value of thegain parameter based upon the first luminance information and theassociated speed.
 4. The imaging system of claim 1, wherein: the firstgroup of photometric parameters includes an exposure parameter, andadjusting one or more values of the first group of photometricparameters comprises adjusting a value of the exposure parameter basedupon the first luminance information and the associated speed.
 5. Theimaging system of claim 1, wherein: the first group of photometricparameters includes an automatic white balance (AWB) parameter, theoperations further comprise modifying a white balance within thereceived information based upon a value of the AWB parameter, andadjusting one or more values of the first group of photometricparameters comprises adjusting the AWB parameter value based upon thefirst luminance information and the associated speed.
 6. The imagingsystem of claim 1, wherein: the first group of photometric parametersincludes a high dynamic range (HDR) parameter, the operations furthercomprise modifying the received first image information based upon avalue of the HDR parameter, and adjusting one or more values of thefirst group of photometric parameters comprises adjusting the HDRparameter value based upon the first luminance information and theassociated speed.
 7. The imaging system of claim 1, wherein theoperations further comprise: determining an image region of interest(ROI) within the first image information that corresponds to an imageobject; determining a radar ROI within the radar information thatcorresponds to a radar object; determining a co-registered ROI pair byco-registering the image ROI with the radar ROI; and determining thatthe image ROI of the co-registered ROI pair corresponds to the object.8. The imaging system of claim 7, wherein the operations furthercomprise associating the speed of the object with the received firstimage information based upon a determination that image ROI of theco-registered ROI pair corresponds to the object.
 9. The imaging systemof claim 7, wherein: the operations further comprise: classifying theimage ROI, using a first trained machine learning engine; andclassifying the radar ROI, using a second trained machine learningengine; and determining the co-registered ROI pair comprises matchingthe image ROIs and the radar ROI based upon a respective image ROIclassification and a respective radar ROI classification.
 10. Theimaging system of claim 9, wherein adjusting the one or more photometricparameters of the first group is based at least in part uponclassification of the object.
 11. The imaging system of claim 1, whereinthe operations further comprise: receiving second image informationincluding another image of the object using a second camera calibratedusing a second group of photometric parameters; determining, using thesecond camera, second luminance information associated with the secondimage information received using the second camera; and adjusting one ormore values of the second group of photometric parameters based upon thesecond luminance information and the associated speed.
 12. The imagingsystem of claim 1, wherein the operations further comprise: receivingsecond image information including another image of the object using asecond camera calibrated using a second group of photometric parameters;determining, using the second camera, second luminance informationassociated with the second image information received using the secondcamera; tracking, using the radar unit, a distance between the objectand the radar unit, adjusting one or more values of the first group ofphotometric parameters based upon the first luminance information, thesecond luminance information, and the tracked distance; and adjustingone or more values of the second group of photometric parameters basedupon the first luminance information, the second luminance information,and the tracked distance.
 13. The imaging system of claim 12, whereinthe operations further comprise determining illumination intensity ofmultiple illumination devices, located at different locations toilluminate the object, based upon the tracked distance.
 14. The imagingsystem of claim 13, wherein: the operations further comprise classifyingthe object, using the radar unit, and determining the illuminationintensity is based at least in part upon classification of the object.15. The imaging system of claim 12, wherein the operations furthercomprise: determining a first image region of interest (ROI) within thefirst image information that corresponds to a first image object;determining a second image ROI within the second image information thatcorresponds to a second image object; determining a radar ROI within theradar information that corresponds to a radar object; determining afirst co-registered ROI pair by co-registering the first image ROI withthe radar ROI; determining a second co-registered ROI pair byco-registering the second image ROI with the radar ROI; determining thatthe first image ROI of the first co-registered ROI pair corresponds tothe object; and determining that the second image ROI of the secondco-registered ROI pair corresponds to the object.
 16. The imaging systemof claim 15, wherein the operations further comprise: associating thespeed of the object with the first image information based upon adetermination that the first image ROI of the first co-registered ROIpair corresponds to the object; and associating the speed of the objectwith the second image information based upon a determination that thesecond image ROI of the second co-registered ROI pair corresponds to theobject.
 17. The imaging system of claim 12, further comprising: theprocessing circuitry; the first camera; the second camera; and the radarunit.
 18. The imaging system of claim 1, further comprising theprocessing circuitry; the first camera; and the radar unit.
 19. A memorystoring instructions which, when executed by processing circuitry, causethe processing circuitry to perform operations comprising: receivingfirst image information including an image of an object, using a firstcamera calibrated using a first group of photometric parameters;determining, using the first camera, respective luminance informationassociated with the received first image information; receiving, using aradar unit, radar information; classifying the object, based upon theradar information, using a trained machine learning engine; andadjusting one or more values of the first group of photometricparameters based upon the determined luminance information andclassification of the object.
 20. The memory of claim 19, wherein theoperations further comprise: receiving second image informationincluding another image of the object, using a second camera calibratedusing a second group of photometric parameters; determining, using thesecond camera, second luminance information associated with the secondimage information received using the second camera; tracking, using theradar unit, a distance between the object and the radar unit; andadjusting one or more values of each of the first and second group ofphotometric parameters based upon the first luminance information, thesecond luminance information, and the classification.
 21. The memory ofclaim 20, wherein the operations further comprise determiningillumination intensity of multiple illumination devices, located atdifferent locations to illuminate the object, based upon the trackeddistance.
 22. A memory storing instructions which, when executed byprocessing circuitry, cause the processing circuitry to performoperations comprising: receiving, using a first camera calibrated usinga first group of photometric parameters that include one or moreadjustable parameters, a first sequence of respective first image framesincluding an image of an object; determining, using the first camera, asequence of respective first luminance information signals correspondingto respective first image frames of the first sequence of respectiveimage frames; tracking, using a radar unit, a speed of the object;determining in response to each of one or more respective received firstimage frames in the sequence of respective first image frames, one ormore values of the one or more of adjustable parameters, based uponrespective corresponding luminance information signals and the trackedspeed of the object; and adjusting the one or more values in responsedetermination of the one or more values.
 23. A calibration methodcomprising: receiving first image information including an image of anobject, using a first camera calibrated using a first group ofphotometric parameters; determining, using the first camera, respectiveluminance information associated with the received first imageinformation; tracking, using a radar unit, a speed of the object; andadjusting values of one or more photometric parameters of the firstgroup of photometric parameters based upon the respective luminanceinformation and the tracked speed of the object.